Method and device to monitor patients with kidney disease

ABSTRACT

A medical monitoring device for monitoring electrical signals from the body of a subject is described. The medical monitoring device monitors electrical signals originating from a cardiac cycle of the subject and associates each cardiac cycle with a time index. The medical monitoring device applies a forward computational procedure to generate a risk score indicative of hyperkalemia, hypokalemia or arrhythmia of the subject. The medical monitoring device can adjust the forward computational procedure based upon clinical data obtained from the subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.15/263,582 filed Sep. 13, 2016, now U.S. Pat. No. 10,207,041, which is aDivisional of U.S. patent application Ser. No. 13/451,461, filed Apr.19, 2012, now U.S. Pat. No. 9,456,755, which is a Continuation-in-partof U.S. patent application Ser. No. 13/424,429, filed Mar. 20, 2012, nowU.S. Pat. No. 9,561,316, which is a Continuation-in-part of U.S. patentapplication Ser. No. 13/424,525, filed Mar. 20, 2012, now U.S. Pat. No.9,700,661, which is a Continuation-in-part of Ser. No. 13/424,479, filedMar. 20, 2012, now U.S. Pat. No. 9,192,707, which claims benefit of andpriority to U.S. Provisional Application No. 61/480,532, filed Apr. 29,2011, which claims benefit of and priority to U.S. ProvisionalApplication No. 61/480,544, filed Apr. 29, 2011.

This application is also a Continuation-in-part of U.S. patentapplication Ser. No. 13/424,429 filed Mar. 20, 2012 now U.S. Pat. No.9,561,316 and is a Continuation-in-part of U.S. patent application Ser.No. 13/424,479 filed Mar. 20, 2012, now U.S. Pat. No. 9,192,707 whichclaims benefit of and priority to U.S. Provisional Application No.61/480,532 filed Apr. 29, 2011. The disclosures of each of theabove-identified applications are hereby incorporated by reference intheir entirety.

FIELD OF THE INVENTION

The invention relates to an electronic medical device for monitoring amammal with kidney disease and issuing alerts if a kidney diseasecondition of the subject worsens. The systems and methods of theinvention include an electronic circuit, sensors, a computer processor,a computational procedure and telecommunication means. The inventionfurther relates to methods for signal processing and parameteridentification.

BACKGROUND

Dialysis simulates kidney function by periodically removing wastesolutes and excess fluid such as urea and ions from a patient's blood.This is accomplished by allowing the body fluids, usually blood, to comeinto close proximity with a dialysate, which is a fluid that serves tocleanse the blood and that actively removes the waste products includingsalts and urea, and excess water. Each dialysis session lasts a fewhours and may typically be repeated as often as three times a week ormore, such as 7 days a week.

Although effective at removing wastes from blood, dialysis treatmentsperformed at dialysis centers are administered intermittently andtherefore fail to replicate the continuous waste removal aspect of anatural and functioning kidney. Once a dialysis session is completed,fluid and other substances such as the sodium and potassium saltsimmediately begin to accumulate again in the tissues of the patient.Notwithstanding the benefits of dialysis, statistics indicate that threeout of five dialysis patients die within five years of commencingtreatment. Studies have shown that increasing the frequency and durationof dialysis sessions can improve the survivability of dialysis patients.Increasing the frequency and duration of dialysis sessions more closelyresembles continuous kidney function. However, the requirement forpatients to travel to the dialysis centers and the costs associated withthe hemodialysis procedure itself pose an upper limit on the frequencyof dialysis procedures.

Another complication is that as blood potassium levels increase betweendialysis sessions, patients become more susceptible to life threateningarrhythmias. Similarly, low concentration of potassium can be dangerousby causing muscle weakness. Significant deviations from a normalphysiological range of potassium must be detected and prevented to avoidworsening of patient conditions. In particular, patients with kidneydisease (KD) are not able to adequately regulate bodily fluid levels andcommon blood solutes such as potassium ion. As such, KD patients are atrisk for developing hyperkalemia (high blood potassium concentration) orhypokalemia (low blood potassium concentration). Normal blood potassiumlevel is from 3.5 to 5.0 mEq; however, KD patients may tend to falloutside this range between treatments. Hyperkalemia and hypokalemia canlead to heart palpitations and arrhythmias.

Since patients with kidney failure cannot effectively eliminatepotassium from their bodies, potassium must be removed duringhemodialysis sessions. Between dialysis sessions of hyperkalemicpatients, serum potassium concentration increases gradually until thenext dialysis session. This increase in the potassium concentrations isa major cause of the increased rate of cardiovascular complications thatis observed in the patients with kidney disease. Approximately 30% ofthese patients have atrial fibrillation, and according to the 2003-2005USRDS data, an additional 6.2% deaths/year are caused by cardiac arrestsor arrhythmias (“Primer on Kidney Diseases”, 5th Ed., A. Greenberg etal., pp 504-5). Hence, there is a clear unmet need for monitoringpatients between dialysis sessions. There is also an unmet need formonitoring and managing hyperkalemia, hypokalemia or arrhythmias inpatients with KD.

In addition to being in danger of exposure to the complications ofabnormal potassium levels between dialysis sessions, many kidneypatients also experience an extreme variation of potassium levels duringtheir dialysis sessions that increases their health risk. Duringhemodialysis, there is a net addition of base in the form ofbicarbonate, which increases the cellular uptake of potassium andattenuates the overall removal of potassium from the cells. Hence,patients may initially experience an increase in their intracellularpotassium levels followed by a reduction in levels resulting inhypokalemia. This condition is of particular concern to patients withunderlying cardiac conditions. As such, there is a clear unmet need toguard against risk to patients during the dialysis sessions and duringthe post-treatment period.

SUMMARY OF THE INVENTION

The invention is directed to a medical device for monitoring subjectswith kidney disease (KD) receiving dialysis treatment. Related medicalsystems and methods for implantable devices as well as externalmonitoring and treatment devices are provided.

In certain embodiments, the medical monitor has a medical device fordetermining body potassium status by monitoring electrical signals ofthe body of a subject, a processor for applying a forward computationalprocedure to the electrical signals monitored from the body incommunication with the implantable medical device, and a communicationsystem indicating a condition of hyperkalemia, hypokalemia or arrhythmiaof the subject wherein the implantable medical device associates acardiac cycle of the subject with a time index and calculates at leastone risk score associated with the time index. The monitoring means canbe implanted or external to the body. The processor is configured toreceive clinical information regarding the physiological state of thesubject associated with the time index and make an adjustment to theforward computational procedure based upon an error between the at leastone risk score and the clinical information.

In certain embodiments, the medical device associates a cardiac cycle ofthe subject with a time index and calculates at least one risk scoreassociated with the time index, and the processor configured to receiveclinical information regarding the physiological state of the subjectassociated with a time index and make an adjustment to the forwardcomputational procedure based upon an error between the at least onerisk score and the clinical information. The medical monitor identifiesa plurality of features from electrical signals monitored from the bodyof a patient, wherein the plurality of features includes one or moreselected from the group consisting of P-R interval, QRS width, Q-Tinterval, QT-dispersion, P-wave amplitude, P-wave peak, S-T segmentdepression, T-wave inversion, U-wave amplitude, T-wave peak amplitude,T-wave morphology (e.g., spiked, rounded, etc.) and heart ratevariability.

In certain embodiments, a medical monitor calculates a disease riskscore from a plurality of features.

In certain embodiments, a first risk score is calculated for a timeindex by applying a first forward computational procedure to one or moreof the features of P-R interval, S-T segment depression, T-waveinversion and U-wave amplitude.

In certain embodiments, a second score is calculated for a time index byapplying a second forward computational procedure to the features of QRSwidth, Q-T interval, P-wave amplitude, P-wave peak, T-wave amplitude,and heart rate variation.

In certain embodiments, a processor of the medical monitor increases analert counter by an incremental amount for each time index where a riskscore exceeds a predetermined threshold and an alert is issued when thealert counter exceeds the predetermined threshold.

In one embodiment, the medical device is implanted and recordsphysiological signals and sends the traces to an external processingunit for interpretation. In another embodiment, the medical devicerecords the physiological signals external to the body and sends thesetraces to an external processing unit for interpretation. Resultinginterpretation is provided to a medical professional as an aid foradditional decisions.

In another embodiment, the medical device records and processes thephysiological signals and sends interpretations of the subject'scondition to the external units. At the same time, the device also warnsthe subject or a care giver with audible warnings or by other means.Resulting interpretation is again provided to a medical professional asan aid for additional decisions.

In another embodiment, parameters of the computational procedure used bythe medical device are determined and adjusted by the medicalprofessional.

In another embodiment, parameters of the computational procedure used bythe medical device are learned by the computational procedure itselfbased on the arrhythmic outcomes of the patient.

In another embodiment, parameters of the computational procedure used bythe medical device are learned by the computational procedure itselfbased on the medical outcomes of the patient, such as hospitalizations.

In certain embodiments, a has the steps of: (i) initiating a blood fluidremoval session with initial system parameters; (ii) acquiring a firstset of data regarding one or more patient physiological parameters;(iii) storing the first data set in a “most effective to date” data setmemory; (iv) associating the initial system parameters in an increasedeffectiveness lookup table with the first data set; (v) adjusting atleast one parameter of the blood fluid removal session to arrive atadjusted system parameters; (vi) acquiring a second set of dataregarding the one or more patient physiological parameters after the atleast one parameter of the blood fluid removal session has beenadjusted; and (vii) if at least one value of the second data set iscloser to the target value than a corresponding at least one value ofthe first data set: replacing the first data set in the most effectiveto date data set memory with the second data set; storing in theincreased effectiveness lookup table data regarding the second data set;and associating data regarding the adjusted system parameters with thesecond data set.

In another embodiment, a method has steps of: (i) storing the first dataset in a least effective to date data set memory; (ii) associating theinitial system parameters in a becoming less effective lookup table withthe first data set prior to adjusting the at least one parameter of theblood fluid removal session; and (iii) if the at least one value of thesecond data set is not closer to the target value than the correspondingat least one value of the first data set: replacing the first data setin the least effective to date data set memory with the second data set;storing in the becoming less effective lookup table data regarding thesecond data set; and associating data regarding the adjusted systemparameters with the second data set.

In one more embodiment, a method has steps of: (i) further adjusting atleast one parameter of the blood fluid removal session to arrive atfurther adjusted system parameters; (ii) acquiring a third set of dataregarding the one or more patient physiological parameters after the atleast one parameter of the blood fluid removal session has been furtheradjusted; and (iii) if at least one value of the third data set iscloser to the target value than a corresponding at least one valuestored in the most effective to date data set memory: replacing the dataset in the most effective to date data set memory with the third dataset; and storing in the increased effectiveness lookup table dataregarding the third data set and associating data regarding the furtheradjusted system parameters with the third data set.

In certain embodiments, a method has the steps of: (i) further adjustingat least one parameter of the blood fluid removal session to arrive atfurther adjusted system parameters; (ii) acquiring a fourth set of dataregarding the one or more patient physiological parameters after the atleast one parameter of the blood fluid removal session has been furtheradjusted; and (iii) if at least one value of the fourth data set is notcloser to the target value than a corresponding at least one valuestored in the least effective to date data set memory: replacing thedata set in the least effective to date data set memory with the fourthdata set; and storing in the becoming less effective lookup table dataregarding the fourth data set and associating data regarding the furtheradjusted system parameters with the fourth data set.

In another embodiment, a method has the steps of: (i) acquiring a fifthset of data regarding one or more patient physiological parameters; (ii)comparing the fifth data set to the increased effectiveness lookuptable; and (iii) adjusting the system parameters the system parametersassociated with the data set stored in the increased effectivenesslookup table if at least one parameter of the data set stored in theimprovement lookup table is within a predetermined range of at least onecorresponding parameter of the fifth data set.

In one more embodiment, a method has the steps of: (i) stopping theblood fluid removal session; (ii) acquiring a sixth set of dataregarding one or more patient physiological parameters; (iii) comparingthe sixth data set to the increased effectiveness lookup table; and (iv)initiating a second blood fluid removal session with the systemparameters associated with the data set stored in the increasedeffectiveness lookup table if at least one parameter of the data setstored in the increased effectiveness lookup table is within apredetermined range of at least one corresponding parameter of the sixthdata set.

In certain embodiments, a method has at least one of the one or morepatient parameters selected from the group consisting of blood pressure,heart rate, pH and concentration of an electrolyte.

In certain embodiments, the electrolyte is potassium.

In certain embodiments, the system parameters have one or more of fluidremoval rate and concentration of one or more electrolyte.

In certain embodiments, a dialysis system has: (a) a blood fluid removalmedium or membrane configured to remove blood from a patient, whereinblood enters the medium, fluid is removed from the blood, and bloodexits the medium; (b) one or more control elements configured to control(i) the rate at which the medium removed fluid from the blood or (ii)the concentration of electrolytes or pH in the blood that exits themedium; (c) one or more sensors configured monitor one or morephysiological parameter of the patient; and (d) control electronicscomprising memory and a processor, wherein the control electronics arein operable communication with the one or more sensors and are operablycoupled to the one or more control elements, wherein the controlelectronics are configured to carry out a method described herein.

In certain embodiments, the blood fluid removal medium or membrane andthe control electronics are housed within a blood fluid removal device.

In certain embodiments, a blood fluid removal or dialysis system has acomputer readable, wherein the computer readable medium comprisesinstructions that cause the control electronics to carry out themethods.

In certain embodiments, a blood fluid removal or dialysis system has:(a) a blood fluid removal medium configured to remove blood from apatient, wherein blood enters the medium, fluid is removed from theblood, and blood exits the medium; (b) one or more control elementsconfigured to control (i) the rate at which the medium removed fluidfrom the blood or (ii) the concentration of electrolytes or pH in theblood that exits the medium; (c) one or more sensors configured monitorone or more physiological parameter of the patient; and (d) controlelectronics comprising memory and a processor, wherein the controlelectronics are in operable communication with the one or more sensorsand are operably coupled to the one or more control elements, whereinthe control electronics are configured to (i) initiate a blood fluidremoval session with initial system parameters; (ii) acquire a first setof data regarding one or more patient physiological parameters; (iii)store the first data set in a most effective to date data set memory;(iv) associate the initial system parameters in an increasedeffectiveness lookup table with the first data set; (v) adjust at leastone parameter of the blood fluid removal session to arrive at adjustedsystem parameters; (vi) acquire a second set of data regarding the oneor more patient physiological parameters after the at least oneparameter of the blood fluid removal session has been adjusted; and(vii) if at least one value of the second data set is closer to a targetvalue than a corresponding at least one value of the first data set:replace the first data set in the most effective to date data set memorywith the second data set; store in the increased effectiveness lookuptable data regarding the second data set; and associate data regardingthe adjusted system parameters with the second data set.

In certain embodiments, a computer-readable medium has instructionsthat, when executed by a blood fluid removal device, cause the device to(i) initiate a blood fluid removal session with initial systemparameters; (ii) acquire a first set of data regarding one or morepatient physiological parameters; store the first data set in a mosteffective to date data set memory; (iii) associate the initial systemparameters in an increased effectiveness lookup table with the firstdata set; (iv) adjust at least one parameter of the blood fluid removalsession to arrive at adjusted system parameters; (v) acquire a secondset of data regarding the one or more patient physiological parametersafter the at least one parameter of the blood fluid removal session hasbeen adjusted; and (vi) if at least one value of the second data set iscloser to a target value than a corresponding at least one value of thefirst data set: replace the first data set in the most effective to datedata set memory with the second data set; store in the increasedeffectiveness lookup table data regarding the second data set; andassociate data regarding the adjusted system parameters with the seconddata set.

In certain embodiments, a method has the steps of: (a) acquiring dataregarding one or more of: (i) one or more patient physiologicalparameters; and (ii) time since last blood fluid removal session; (b)acquiring data regarding one or more target outcomes of a blood fluidremoval session; (c) comparing the data regarding at least one of theone or more target outcomes of the blood fluid session to correspondingdata regarding at least one prior patient outcome stored in a lookuptable, wherein the lookup table comprises data regarding systemparameters used in one or more prior blood fluid removal sessions of thepatient and comprises patient data prior to the previous sessionregarding one or more of (i) one or more patient physiologicalparameters; and (ii) time since last blood fluid removal session; (d)comparing the data regarding the one or more of (i) one or more patientphysiological parameters; and (ii) time since last blood fluid removalsession to corresponding patient data prior to the previous sessionstored in the lookup table; and (e) initiating a blood fluid removalsession employing the system parameters used the prior blood fluidremoval session if the at least one of the one or more target outcomesis within a predetermined range of the corresponding data regarding theat least one prior patient outcome stored in the lookup table and thedata regarding the one or more of (i) one or more patient physiologicalparameters; and (ii) time since last blood fluid removal session iswithin a predetermined range of the corresponding patient data prior tothe previous session stored in the lookup table.

In certain embodiments, a method has at least one of the one or morepatient parameters selected from the group consisting of blood pressure,heart rate, pH and concentration of an electrolyte.

In certain embodiments, the system parameters are one or more of fluidremoval rate and concentration of one or more electrolyte.

In certain embodiments, a blood fluid removal system has: (a) a bloodfluid removal medium configured to remove blood from a patient, whereinblood enters the medium, fluid is removed from the blood, and bloodexits the medium; (b) one or more control elements configured to control(i) the rate at which the medium removed fluid from the blood or (ii)the concentration of electrolytes or pH in the blood that exits themedium; (c) one or more sensors configured monitor one or morephysiological parameter of the patient; (d) an input configured to allowentry of data regarding patient or system parameters; and (e) controlelectronics comprising memory and a processor, wherein the controlelectronics are in operable communication with the one or more sensorsand are operably coupled to the one or more control elements and theinput, wherein the control electronics are configured to carry out amethod described herein.

In certain embodiments, the blood fluid removal medium or membrane andthe control electronics are housed within a blood fluid removal ordialysis device.

In certain embodiments, a blood fluid removal or dialysis system has acomputer readable, wherein the computer readable medium has instructionsthat cause control electronics to carry out a method described herein.

In certain embodiments, a blood fluid removal or dialysis system has:(a) a blood fluid removal medium configured to remove blood from apatient, wherein blood enters the medium, fluid is removed from theblood, and blood exits the medium; (b) one or more control elementsconfigured to control (i) the rate at which the medium removed fluidfrom the blood or (ii) the concentration of electrolytes or pH in theblood that exits the medium; (c) one or more sensors configured monitorone or more physiological parameter of the patient; (d) an inputconfigured to allow entry of data regarding patient or systemparameters; and (e) control electronics comprising memory and aprocessor, wherein the control electronics are in operable communicationwith the one or more sensors and are operably coupled to the one or morecontrol elements and the input, wherein the control electronics areconfigured to: (i) acquire data regarding one or more of: one or morepatient physiological parameters; and time since last blood fluidremoval session; (ii) acquire data regarding one or more target outcomesof a blood fluid removal session; (iii) compare the data regarding atleast one of the one or more target outcomes to corresponding dataregarding at least one prior patient outcome stored in a lookup table,wherein the lookup table comprises data regarding system parameters usedin one or more prior blood fluid removal sessions of the patient andcomprises patient data prior to the previous session regarding one ormore of (i) one or more patient physiological parameters; and (ii) timesince last blood fluid removal session; (iv) compare the data regardingthe one or more of (i) one or more patient physiological parameters; and(ii) time since last blood fluid removal session to correspondingpatient data prior to the previous session stored in the lookup table;and (v) initiate a blood fluid removal session employing the systemparameters used in the prior blood fluid removal session if the at leastone of the one or more target outcomes is within a predetermined rangeof the corresponding data regarding the at least one prior patientoutcome stored in the lookup table and the data regarding the one ormore of (i) one or more patient physiological parameters; and (ii) timesince last blood fluid removal session is within a predetermined rangeof the corresponding patient data prior to the previous session storedin the lookup table.

In certain embodiments, a computer-readable medium has instructionsthat, when executed by a blood fluid removal or dialysis device, causethe device to (i) acquire data regarding one or more of: one or morepatient physiological parameters; and time since last blood fluidremoval session; (ii) acquire data regarding one or more target outcomesof a blood fluid removal session; (iii) compare the data regarding theat least one of the one or more target outcomes to corresponding dataregarding at least one prior patient outcome stored in a lookup table,wherein the lookup table comprises data regarding system parameters usedin one or more prior blood fluid removal sessions of the patient andcomprises patient data prior to the previous session regarding one ormore of one or more patient physiological parameters and time since lastblood fluid removal session; (iv) compare the data regarding the one ormore of (i) one or more patient physiological parameters; and (ii) timesince last blood fluid removal session to corresponding patient dataprior to the previous session stored in the lookup table; and (v)initiate a blood fluid removal session employing the system parametersused in the prior blood fluid removal session if the at least one of theone or more target outcomes is within a predetermined range of thecorresponding data regarding the at least one prior patient outcomestored in the lookup table and the data regarding the one or more of (i)one or more patient physiological parameters; and (ii) time since lastblood fluid removal session is within a predetermined range of thecorresponding patient data prior to the previous session stored in thelookup table.

In certain embodiments, a method has the steps of: (i) collecting firstdata regarding a patient, the data including one or more of aphysiological parameter and time since last blood fluid removal session;(ii) collecting second data regarding system parameters employed inblood fluid removal sessions of the patient; (iii) determining, based onthe first and second collected data, whether at least one physiologicalparameter of the patient became more effective as a result of the systemparameters employed; (iv) determining whether a value of current patientdata is within a predetermined range of a corresponding value of firstcollected data; and (v) employing the system parameters that resulted inincreased effectiveness, if such parameters are determined to exist andif the current patient data is determined to be within the predeterminedrange.

In certain embodiments, a blood fluid removal or dialysis system has:(a) a blood fluid removal medium configured to remove blood from apatient, wherein blood enters the medium, fluid is removed from theblood, and blood exits the medium; (b) one or more control elementsconfigured to control (i) the rate at which the medium removed fluidfrom the blood or (ii) the concentration of electrolytes or pH in theblood that exits the medium; (c) one or more sensors configured monitorone or more physiological parameter of the patient; (d) an inputconfigured to allow entry of data regarding patient or systemparameters; and (e) control electronics comprising memory and aprocessor, wherein the control electronics are in operable communicationwith the one or more sensors and are operably coupled to the one or morecontrol elements and the input, wherein the control electronics areconfigured to carry out a method described herein.

In certain embodiments, a blood fluid removal system or dialysis systemhas a computer readable media, wherein the computer readable mediacomprises instructions that cause control electronics to carry out amethod described herein.

In certain embodiments, a system has: (a) a blood fluid removal mediumconfigured to remove blood from a patient, wherein blood enters themedium, fluid is removed from the blood, and blood exits the medium; (b)one or more control elements configured to control (i) the rate at whichthe medium removed fluid from the blood or (ii) the concentration ofelectrolytes or pH in the blood that exits the medium; (c) one or moresensors configured monitor one or more physiological parameter of thepatient; (d) an input configured to allow entry of data regardingpatient or system parameters; and (e) control electronics comprisingmemory and a processor, wherein the control electronics are in operablecommunication with the one or more sensors and are operably coupled tothe one or more control elements and the input, wherein the controlelectronics are configured to: (i) collect first data regarding apatient, the data including one or more of a physiological parameter andtime since last blood fluid removal session; (ii) collect second dataregarding system parameters employed in blood fluid removal sessions ofthe patient; (iii) determine, based on the first and second collecteddata, whether at least one physiological parameter of the patient becamemore effective as a result of the system parameters employed; (iv)determine whether a value of current patient data is within apredetermined range of a corresponding value of first collected data;and (v) employ the system parameters that resulted in increasedeffectiveness, if such parameters are determined to exist and if thecurrent patient data is determined to be within the predetermined range.

In certain embodiments, a computer-readable medium has instructionsthat, when executed by a blood fluid removal device, cause the device to(i) collect first data regarding a patient, the data including one ormore of a physiological parameter and time since last blood fluidremoval session; (ii) collect second data regarding system parametersemployed in blood fluid removal sessions of the patient; (iii)determine, based on the first and second collected data, whether atleast one physiological parameter of the patient became more effectiveas a result of the system parameters employed; (iv) determine whether avalue of current patient data is within a predetermined range of acorresponding value of first collected data; and (v) employ the systemparameters that resulted in increased effectiveness, if such parametersare determined to exist and if the current patient data is determined tobe within the predetermined range.

In certain embodiments, a method has the steps of: (i) storing systemparameters from a first blood fluid removal session in memory; (ii)acquiring a first set of data regarding one or more patient parametersfollowing the first session but before a second session; (iii) storingthe first data set in a most effective to date data set memory; (iv)associating the first system parameters in an increased effectivenesslookup table with the first data set; (v) storing system parameters fromthe second blood fluid removal session in memory; (vi) acquiring asecond set of data regarding the one or more patient parametersfollowing the second session; (vii) determining whether at least onevalue of the second data set is closer to a target value than at leastone corresponding value of the first data set; and (viii) if the atleast one value of the second data set is determined to be closer to thetarget value than the corresponding at least one value of the first dataset: replacing the first data set in the most effective to date data setmemory with the second data set; storing in the increased effectivenesslookup table data regarding the second data set; and associating dataregarding the second system parameters with the second data set.

In certain embodiments, a method has the steps of: (i) storing the firstdata set in a least effective to date data set memory; (ii) associatingthe first system parameters in a decreased effectiveness lookup tablewith the first data set; and (iii) if the at least one value of thesecond data set is determined not to be closer to the target value thanthe corresponding at least one value of the first data set: replacingthe first data set in the least effective to date data set memory withthe second data set; storing in the decreased effectiveness lookup tabledata regarding the second data set; and associating data regarding thesecond system parameters with the second data set.

In certain embodiments, a method has the steps of: (i) storing systemparameters for a third blood fluid removal session in memory; (ii)acquiring a third set of data regarding the one or more patientparameters following the third session; (iii) determining whether atleast one value of the third data set is closer to a target value thanat least one corresponding value stored in the most effective to datedata set memory; and (iv) if the at least one value of the third dataset is determined to be closer to the target value than thecorresponding at least one value stored in the most effective to datedata set memory: replacing the data set in the most effective to datedata set memory with the third data set; and storing in the increasedeffectiveness lookup table data regarding the third data set andassociating data regarding the third system parameters with the thirddata set.

In certain embodiments, a method has the steps of: (i) storing systemparameters from a fourth blood fluid removal session in memory; (ii)acquiring a fourth set of data regarding the one or more patientparameters following the fourth session; (iii) determining whether atleast one value of the fourth data set is further from a target valuethan at least one corresponding value stored in the least effective todate data set memory; and (iv) if the at least one value of the fourthdata set is determined not to be closer to the target value than thecorresponding at least one value stored in the least effective to datedata set memory: replacing the data set in the least effective to datedata set memory with the fourth data set; and storing in the decreasedeffectiveness lookup table data regarding the fourth data set andassociating data regarding the fourth system parameters with the fourthdata set.

In certain embodiments, a method has the steps of: (i) acquiring a fifthset of data regarding one or more patient parameters; (ii) consultingthe increased effectiveness lookup table to determine whether at leastone parameter of a data set stored in the increased effectiveness lookuptable is within a predetermined range of the fifth data set; and (iii)setting system parameters for a next blood fluid removal session to thesystem parameters associated with the data set stored in the increasedeffectiveness lookup table.

In certain embodiments, at least one of the one or more patientparameters are selected from the group consisting of blood pressure,heart rate, pH and concentration of an electrolyte.

In certain embodiments, the system parameters have one or more of fluidremoval rate and concentration of one or more electrolyte.

In certain embodiments, the method is carried out by a blood fluidremoval system.

In certain embodiments, a blood fluid removal system has the steps of:(a) a blood fluid removal medium configured to remove blood from apatient, wherein blood enters the medium, fluid is removed from theblood, and blood exits the medium; (b) one or more control elementsconfigured to control (i) the rate at which the medium removed fluidfrom the blood or (ii) the concentration of electrolytes or pH in theblood that exits the medium; (c) one or more sensors configured monitorone or more physiological parameter of the patient; and (d) controlelectronics comprising memory and a processor, wherein the controlelectronics are in operable communication with the one or more sensorsand are operably coupled to the one or more control elements.

In certain embodiments, the blood fluid removal medium and the controlelectronics are housed within a blood fluid removal device.

In certain embodiments, a blood fluid removal system has a computerreadable media, wherein the computer readable media has instructionsthat cause control electronics to carry out a method described herein.

In certain embodiments, a system has: (a) a blood fluid removal mediumconfigured to remove blood from a patient, wherein blood enters themedium, fluid is removed from the blood, and blood exits the medium; (b)one or more control elements configured to control (i) the rate at whichthe medium removed fluid from the blood or (ii) the concentration ofelectrolytes or pH in the blood that exits the medium; (c) one or moresensors configured monitor one or more physiological parameter of thepatient; and (d) control electronics comprising memory and a processor,wherein the control electronics are in operable communication with theone or more sensors and are operably coupled to the one or more controlelements, wherein the control electronics are configured to (i) storesystem parameters from a first blood fluid removal session in memory;(ii) acquire a first set of data regarding one or more patientparameters following the first session but before a second session;(iii) store the first data set in a most effective to date data setmemory; (iv) associate the first system parameters in an increasedeffectiveness lookup table with the first data set; (v) store systemparameters from the second blood fluid removal session in memory; (vi)acquire a second set of data regarding the one or more patientparameters following the second session; (vii) determine whether atleast one value of the second data set is closer to a target value thanat least one corresponding value of the first data set; and (viii) ifthe at least one value of the second data set is determined to be closerto the target value than the corresponding at least one value of thefirst data set: replace the first data set in the most effective to datedata set memory with the second data set; store in the increasedeffectiveness lookup table data regarding the second data set; andassociate data regarding the second system parameters with the seconddata set.

In certain embodiments, a computer-readable medium has instructionsthat, when executed by a blood fluid removal device, cause the device to(i) store system parameters from a first blood fluid removal session inmemory; (ii) acquire a first set of data regarding one or more patientparameters following the first session but before a second session;(iii) store the first data set in a most effective to date data setmemory; (iv) associate the first system parameters in an increasedeffectiveness lookup table with the first data set; (v) store systemparameters from the second blood fluid removal session in memory; (vi)acquire a second set of data regarding the one or more patientparameters following the second session; (vii) determine whether atleast one value of the second data set is closer to a target value thanat least one corresponding value of the first data set; and (viii) ifthe at least one value of the second data set is determined to be closerto the target value than the corresponding at least one value of thefirst data set: replace the first data set in the most effective to datedata set memory with the second data set; store in the increasedeffectiveness lookup table data regarding the second data set; andassociate data regarding the second system parameters with the seconddata set.

In certain embodiments, a method has the steps of (i) identifying apatient for which a blood fluid removal session is indicated; and (ii)chronically monitoring an indicator of blood electrolyte concentrationor blood pH of the patient via an implantable sensor device.

In certain embodiments, a method has the steps of: (i) determiningwhether the monitored indicator crosses a predetermined threshold; and(ii) alerting the patient if the indicator is determined to cross thethreshold.

In certain embodiments, a method has the step of alerting a healthcareprovider if the indicator is determined to cross the threshold.

In certain embodiments, a method has the step of determining anappropriate electrolyte concentration or buffer concentration for afluid to be used in a blood fluid removal session based on the monitoredindicator.

In certain embodiments, a fluid to be used in a blood fluid removal ordialysis session comprises dialysate fluid.

In certain embodiments, a fluid to be used in a blood fluid removalsession or dialysis session comprises replacement fluid.

In certain embodiments, a method has the step of transmitting dataregarding a monitored indictor to a blood fluid removal device, orcontrol electronics configured to control a blood fluid removal device,wherein the blood fluid removal or dialysis device, monitoring device orcontrol electronics determines the appropriate electrolyte concentrationor buffer concentration.

In certain embodiments, monitoring includes monitoring the indicator viaan implantable sensor.

In certain embodiments, a method has the step of: monitoring anindicator via an external sensor, and calibrating an implantable sensorbased on data acquired from the external sensor.

In certain embodiments, monitoring via an external sensor occurs duringa blood fluid removal or dialysis session, and wherein the calibratingoccurs during a blood fluid removal or dialysis session.

In certain embodiments, a method has the steps of: (i) chronicallymonitoring, via an implantable sensor, an indicator of blood electrolyteconcentration or blood pH of the patient during the blood fluid removalsession; and (ii) initiating blood fluid removal procedure for a patientin need thereof, wherein the procedure comprises use of a dialysatefluid and a dialysate membrane, as at least a part of a blood fluidremoval medium or membrane, across which electrolytes may be exchangedbetween blood and dialysate fluid, wherein the concentration ofelectrolyte in the dialysate fluid is based on a value of the monitoredindicator.

In certain embodiments, a method has the steps of: (i) chronicallymonitoring, via an implantable sensor, an indicator of blood electrolyteconcentration or blood pH of the patient during the blood fluid removalsession; and (ii) initiating blood fluid removal procedure for a patientin need thereof, wherein the procedure comprises use of a dialysatefluid and a dialysate membrane, as at least a part of a blood fluidremoval medium or membrane, across which electrolytes may be exchangedbetween blood and dialysate fluid, wherein the rate of flow of thedialysate fluid or the blood is based on a value of the monitoredindicator.

Other objects, features and advantages of the present invention willbecome apparent to those skilled in the art from the following detaileddescription. It is to be understood, however, that the detaileddescription and specific examples, while indicating some embodiments ofthe present invention are given by way of illustration and notlimitation. Many changes and modifications within the scope of thepresent invention may be made without departing from the spirit thereof,and the invention includes all such modifications.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 is an exemplary embodiment of an EKG monitor.

FIG. 2 is an exemplary embodiment of an EKG monitor having additionalfunctionality to supply an electrical stimulation to muscle tissue and asensor to observe a mechanical response.

FIG. 3 is an illustrative mechanical response of muscle tissue toelectrical stimulation depending upon a potassium environment.

FIG. 4 is a graphical representation of discrete computationalprocedures to determine feature scores in accordance with someembodiments of the invention.

FIG. 5 is a graphical representation of continuous computationalprocedures to determine feature scores in accordance with someembodiments of the invention.

FIG. 6 is a flow chart of a process to issue an alert in accordance withsome embodiments.

FIG. 7 shows a disease risk score trend.

FIG. 8 shows the application of a correction to minimize error inaccordance with some embodiments.

FIG. 9 shows a monitoring of a medical system or device in accordancewith some embodiments.

FIG. 10 shows an additional system for monitoring a medical device inaccordance with some embodiments.

FIG. 11 shows the acquisition of feature values for an ECG.

FIG. 12 shows a process for setting feature scores on a common scale inaccordance with some embodiments.

FIG. 13 shows a process for issuing an alert for hypokalemia orhyperkalemia in accordance with some embodiments.

FIGS. 14-18 show flow diagrams illustrating methods in accordance withcertain embodiments described herein.

FIGS. 19-25 show flow diagrams illustrating methods in accordance withcertain embodiments described herein.

FIG. 26 shows a schematic graphical representation of monitoredprophetic data shown for purposes of illustration.

DETAILED DESCRIPTION OF THE INVENTION

Unless defined otherwise, all technical and scientific terms used hereingenerally have the same meaning as commonly understood by one ofordinary skill in the relevant art.

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e., to at least one) of the grammatical object of thearticle. By way of example, “an element” means one element or more thanone element.

“Chronic kidney disease” (CKD) is a condition characterized by the slowloss of kidney function over time. The most common causes of CKD arehigh blood pressure, diabetes, heart disease, and diseases that causeinflammation in the kidneys. Chronic kidney disease can also be causedby infections or urinary blockages. If CKD progresses, it can lead toend-stage renal disease (ESRD), where the kidneys function is inadequateto sustain life without supplemental treatment.

The terms “communicate” and “communication” include but are not limitedto, the connection of system electrical elements, either directly orwirelessly, using optical, electromagnetic, electrical or mechanicalconnections, for data transmission among and between said elements.

The term “comprising” includes, but is not limited to, whatever followsthe word “comprising.” Thus, use of the term indicates that the listedelements are required or mandatory but that other elements are optionaland may or may not be present.

The term “consisting of” includes and is limited to whatever follows thephrase the phrase “consisting of.” Thus, the phrase indicates that thelimited elements are required or mandatory and that no other elementsmay be present.

A “control system” consists of combinations of components that acttogether to maintain a system to a desired set of performancespecifications. The performance specifications can include sensors andmonitoring components, processors, memory and computer componentsconfigured to interoperate.

A “controller” or “control unit” is a device which monitors and affectsthe operational conditions of a given system. The operational conditionsare typically referred to as output variables of the system, which canbe affected by adjusting certain input variables.

A “patient” is a member of any animal species, preferably a mammalianspecies, optionally a human. The subject can be an apparently healthyindividual, an individual suffering from a disease, or an individualbeing treated for an acute condition or a chronic disease.

The term “programmable” as used herein refers to a device using computerhardware architecture and being capable of carrying out a set ofcommands, automatically.

The term “sensory unit” refers to an electronic component capable ofmeasuring a property of interest.

The terms “treating” and “treatment” refer to the management and care ofa patient having a pathology or condition. Treating includesadministering one or more embodiments of the present invention toprevent or alleviate the symptoms or complications or to eliminate thedisease, condition, or disorder.

As used herein, “treatment” or “therapy” refers to both therapeutictreatment and prophylactic or preventative measures. “Treating” or“treatment” does not require complete alleviation of signs or symptoms,does not require a cure, and includes protocols having only a marginalor incomplete effect on a patient.

Electrocardiogram or ECG is a time varying waveform, produced by theelectrical activity of the cardiac muscle and the associated electricalnetwork within the myocardium. Term is used interchangeably for thetracing that is available from the surface of the subject, or from animplantable or external device.

The term “P-R interval” refers to the length of time from the beginningof the P wave to the beginning of the QRS complex.

The term “QRS width” refers to the length of time of the QRS complex.

The term “Q-T interval” refers to the length of time from the beginningof the QRS complex to the end of the T-wave.

The term “Q-T dispersion” refers to the difference between the maximumand minimum QT intervals measured in a time period.

The term “P-wave amplitude” refers to the maximum potential reached bythe P-wave.

The term “P-wave peak” refers to the rate of change in the P wave inunits of potential change per unit time.

The term “S-T segment” refers to the interval between the QRS complexand the beginning of the T wave. S-T segment is depressed if it has adownward concavity.

The term “T wave” refers to the wave after the QRS complex and the S-Tsegment. An inverted T wave has a negative amplitude.

The term “U wave amplitude” refers to the maximum potential of a wavethat follows the T wave. The U wave is not always observed in a cardiaccycle.

The term “heart rate variability” refers to the time difference betweenthe peaks of R-waves over time in cardiac cycles.

The term “scalar quantity” or “scalar value” refers to a property, valueor quantity that is completely expressed in terms of magnitude.

The term “feature,” “cardiac feature,” “ECG feature” or “feature of acardiac cycle” refers to a property of the cardiac cycle, as observed byECG or other means, that is reducible to numerical form. Featuresinclude, but are not limited to, P-R interval, QRS width, Q-T interval,P-wave amplitude, S-T segment depression, T wave inversion, U waveamplitude and T wave amplitude.

The term “feature value” refers to a feature of a cardiac cycleexpressed as a scalar quantity or qualitative property such as depressedor inverted.

The term “feature score” refers to a feature value that has beenconverted to a common scale.

The term “common scale” refers to a unitless scale for expressingfeature values where the common scale has a minimum possible value and amaximum possible value and the feature values differ in units or lack acommon range of magnitude. In some embodiments, the common scale has aminimum value of 0 and a maximum value of 1.

The term “determinant” or “determinate value” refers to a quantity orcriterion that a feature value or feature score is compared to for thepurposes of calculating a risk score.

The term “risk score” or “disease risk score” refers to value calculatedwith one or more feature values or scores that indicates an undesirablephysiological state of the patient.

The term “exponential factor,” “value k,” or “variable k” refers to amodifiable variable present in an exponent (e.g. e^(k)) in acomputational procedures used to convert a feature value to a featurescore.

The term “weighting factor” or “weighting coefficient” refers to anadjustable coefficient to terms for addition to calculate a disease riskscore.

The term “hypokalemia” refers to a physiological state wherein theconcentration of potassium ions in the blood serum or interstitial fluidis less than the normal physiological range of 3.5 to 5 mEq/L.

The term “hyperkalemia” refers to a physiological state wherein theconcentration of potassium ions in the blood serum or interstitial fluidis more than the normal physiological range of 3.5 to 5 mEq/L.

“Kidney disease” (KD) is a condition characterized by the slow loss ofkidney function over time. The most common causes of KD are high bloodpressure, diabetes, heart disease, and diseases that cause inflammationin the kidneys. Kidney disease can also be caused by infections orurinary blockages. If KD progresses, it can lead to end-stage renaldisease (ESRD), where kidney function is inadequate to sustain lifewithout supplemental treatment. KD can be referred to by differentstages indicated by Stages 1 to 5. Stage of KD can be evaluated byglomerular filtration rate of the renal system. Stage 1 KD can beindicated by a GFR greater than 90 mL/min/1.73 m² with the presence ofpathological abnormalities or markers of kidney damage. Stage 2 KD canbe indicated by a GFR from 60-89 mL/min/1.73 m², Stage 3 KD can beindicated by a GFR from 30-59 mL/min/1.73 m² and Stage 4 KD can beindicated by a GFR from 15-29 mL/min/1.73 m². A GFR less than 15mL/min/1.73 m² indicates Stage 5 KD or ESRD. It is understood that KD,as defined in the present invention, contemplates KD regardless of thedirection of the pathophysiological mechanisms causing KD and includesCRS Type II and Type IV and Stage 1 through Stage 5 KD among others.Kidney disease can further include acute renal failure, acute kidneyinjury, and worsening of renal function. In the Cardiorenal Syndrome(CRS) classification system, CRS Type I (Acute Cardiorenal Syndrome) isdefined as an abrupt worsening of cardiac function leading to acutekidney injury; CRS Type II (Chronic Cardiorenal syndrome) is defined aschronic abnormalities in cardiac function (e.g., chronic congestiveheart failure) causing progressive and permanent kidney disease; CRSType III (Acute Renocardiac Syndrome) is defined as an abrupt worseningof renal function (e.g., acute kidney ischaemia or glomerulonephritis)causing acute cardiac disorders (e.g., heart failure, arrhythmia,ischemia); CRS Type IV (Chronic Renocardiac syndrome) is defined askidney disease (e.g., chronic glomerular disease) contributing todecreased cardiac function, cardiac hypertrophy and/or increased risk ofadverse cardiovascular events; and CRS Type V (Secondary CardiorenalSyndrome) is defined as a systemic condition (e.g., diabetes mellitus,sepsis) causing both cardiac and renal dysfunction (Ronco et al.,Cardiorenal syndrome, J. Am. Coll. Cardiol. 2008; 52:1527-39).

Monitoring of Dialysis Treatment

As discussed above, a patient's serum potassium level can be unstableand/or drift after dialysis treatment. Due to the requirement for properpolarization for cardiac function, changes in potassium serum levelsafter treatment are a contributor to arrhythmias and other cardiaccomplications in patients undergoing kidney dialysis therapy. Duringdialysis treatment, small solutes in the blood or other body fluids,such as potassium ions, freely interchange with a dialysate fluid.However, due to the action of the sodium-potassium pump, the vastmajority of potassium in the body is present intracellularly and notdirectly accessible during dialysis. Due to the sequestering ofpotassium within cells, potassium serum levels can change significantlyfollowing dialysis treatment sessions. Specifically, dialysis treatmentcan enhance the movement of potassium ions into the cells, which canefflux out of the cells following treatment leading to significantchanges in potassium ion concentration over time.

Normal serum potassium level ranges from 3.5 to 5 mEq/L, wherein adialysate solution is at a lower concentration to drive the movement ofpotassium ions from the serum to the dialysate. As dialysis functions toremove potassium ions from the blood serum as a result of aconcentration gradient between the patient's blood serum and thedialysate, additional potassium ions are drawn out from cells into theintracellular fluids to provide for further removal of potassium ions.However, the movement of potassium ions from inside cells to theextracellular fluids is not consistent in all patients. In particular,acid-base balance can affect the influx and efflux of potassium ionsfrom cells. Tonicity, glucose and insulin concentrations andcatecholamine activity also affect the balance of potassium betweencells and the extracellular fluid. Patients can experience slightalkalosis during at the beginning of dialysis treatment, which canpersist during a multi-hour dialysis treatment. Alkalosis is caused bythe bicarbonate present in the dialysate, which acts as a pH buffer.During alkalosis, it is possible for intracellular potassium ionconcentrations to increase even while the serum potassium ionconcentration is simultaneously being reduced by dialysis. As such, therate of potassium removal is not uniform during dialysis.

At the end of dialysis treatment, an efflux of intracellular potassiumback into the blood serum can result in hyperkalemia. Hyperkalemia canalso occur through the accumulation of potassium in the patient's diet.Conversely, potassium in the blood serum can remain low followingdialysis resulting in hypokalemia. The innovations disclosed hereinenable the monitoring of a patient's serum potassium level duringdialysis, after dialysis or both during and after dialysis. In certainembodiments, ECG signals from the patient can be evaluated to determinepotassium status. For example, hyperkalemia can cause a reduction in Pwave amplitude, peaked or inverted T waves as well as changes in thetime width of the QRS complex.

Using the innovations described herein, a patient can be monitored forpotentially life-threatening hyperkalemia or hypokalemia after adialysis session possibly before the patient becomes aware of symptoms.In certain embodiments, the information gained regarding the patient'sblood serum potassium levels following dialysis can be used to adjustdialysis treatments provided to that patient. For example, a patientthat shows a pattern of a high serum potassium levels after dialysistreatment be administered treatment where the amount of potassium saltin the dialysate fluid is adjusted, for example by a gradient, from ahigh concentration at the beginning of dialysis to a lower concentrationat the end of dialysis to reduce the large changes in potassium plasmalevels during treatment that can result in hyperkalemia. Alternatively,a patient showing a tendency toward hyperkalemia can receive morefrequent treatments and/or more frequent treatments of shorter durationto affect a greater degree of potassium removal. A patient can even beadvised to modify their diet passed upon blood serum potassium levelsfollowing dialysis. Similarly, a patient showing a tendency towardhypokalemia following dialysis can receive less frequent treatment ortreated with a dialysate fluid having a higher concentration ofpotassium salt.

In some embodiments, serum potassium concentration, electrolyte levelsand or pH can be monitored before and/or during a dialysis treatment forbetter management of electrolytes, including potassium, in the patient.Any suitable transducer or sensor can be employed to detect pH orvarious electrolytes in the blood prior to initiation of a dialysistreatment. In embodiments, the transducer or sensor is an ion-selectiveelectrode configured to detect H⁺ ions (pH), K⁺ ions, Na⁺ ions, Ca²⁺ions, Cl⁻ ions, phosphate ions, magnesium ions, acetate ions, aminoacids ions, or the like. Data from the pH and/or ion sensors/electrodescan be employed to appropriately select an initial dialysate compositionprior to the beginning of a dialysis treatment. Data acquired from thesensors can be transmitted to a processor or other device or devices incommunication with a dialysis treatment system, wherein the initial pHand electrolyte composition of a dialysate or a replacement fluid can beadjusted. The pH and electrolyte concentration of the fluid (dialysateor replacement fluid) can be adjusted in any suitable manner.

In particular, data from pH and/or ion sensors/electrodes can betransmitted to be available to a healthcare provider through theprocessor or other device and used to adjust the concentration ofelectrolytes or pH in a dialysate or replacement fluid. In someembodiments, the dialysate is generated from water or alow-concentration solution present in a dialysate circuit in fluidcommunication with the patient, wherein one or more pumps controls theaddition of one or more infusate solutions to the dialysate circuit toconstitute a desired dialysate immediately prior to contact with thepatient or a hemodialyzer. The dialysate can be constitute to affect aspecific mass transfer of electrolytes from the blood of a patient tothe dialysate or from the dialysate to the blood of a patient in amanner to correct any determined electrolyte imbalances or non-idealelectrolyte ranges. Similarly, the amount of a buffer, such asbicarbonate, in the dialysate can be adjusted to vary the amount ofbicarbonate uptake by the patient during treatment.

Medical Device

The systems and medical devices of the present invention monitorphysiological signals from patients. The medical devices provide manyadvantages including full patient compliance, complete patient mobility,lower maintenance requirements and lower chances for device relatedinfections. The medical devices can be powered with internal batteriesand can be implanted or external to the body. Data transmission to andfrom the devices is accomplished by electromagnetic or electroconductivetelemetry means. In embodiments of the invention, the medical devicescontain one or multiple sets of sensors. For example, the devices cansense the ECG of a patient and change in activity or posture of thepatient. The sensed signals can be stored in memory and transmitted viaradio telemetry. Furthermore, the processor units within the medicaldevices can be used to process the detected or recorded signals.

The ECG signals can be processed to extract features from the ECGsignal. These features include but are not limited to P-R interval, QRSwidth, Q-T interval, QT-dispersion, P-wave amplitude, P-wave peak, S-Tsegment depression, Inverted T-waves, U-wave observation, T-wave peakamplitude, Heart Rate Variability. While some features are measured foreach cardiac cycle such as the P-R interval, others are calculated as atime average such as heart rate variability.

Many factors affect the features of the ECG. For example, heart ratevaries as a result of changes in metabolic demand. During exercise, anincreased demand for oxygen causes the heart rate to increase.Correspondingly, the P-R interval decreases during exercise. Anotherfactor that modulates the features of the ECG is changes in theconcentrations of the ions in the body. An ion that modulates the ECGand is important for the management of KD patients is potassium ion. Ingeneral, changes in potassium concentrations manifest as alterations ofsome of the features of the ECG. However, these alterations vary fromone patient to another patient and can necessitate the individualizationof the detection computational procedure as described herein.

In particular, the medical device of the present invention monitors apatient electrocardiogram (ECG) wherein an internal or externalprocessing unit extracts features from the ECG and processes theresulting data. An optional telemetry system or any other alert system,such as an audio feedback device, can communicate the results to thepatient and medical care personnel as needed. In certain embodiments,the device has an electrical pulse generator configured to contact thetissue of a patient such as muscle tissue or cardiac tissue, and asensor to detect a response of the tissue where the response provides anindication of the potassium ion concentration in the extracellularfluid. In another embodiment, the device comprises a pulse generatorconfigured to generate electrical stimulation wherein an electrodedelivers electrical stimulation to a tissue such as a skeletal muscle ina patient. The device can include a sensor configured to detect at leastone response of the tissue to electrical stimulation, and a processorconfigured to determine a concentration of potassium ions in theextracellular fluid of the patient as a function of the response. Inparticular, the processor can be configured to determine a concentrationof potassium ions as a function of a sustained contraction of thetissue, for example, or a rippled contraction of the tissue, a rate ofrelaxation of the tissue, a pulse width of the response, the occurrenceof summation in the response or the amplitude of the response. Thesystem can be external, partially implantable or fully implantable.Notably, a healthy level of potassium in the human blood is about 3.5-5mEq/L, but in patients with KD, the concentration could rise to 6-8 mM.Most patients are dialyzed with hypo-osmotic dialysate solutions wherethe potassium concentration is fixed at a hypo-osmotic level, such as 2mM, to assure the transfer of potassium ions from the patient's bloodinto the dialysate solution.

The medical device can be a unit with no leads or may contain leads andexternal sensors. Units with no leads such as the Medtronic Reveal®device, or other known devices familiar to those of ordinary skill, mayhave electrodes for sensing electrocardiograms or for deliveringelectrical stimulation. Units with leads, such as pacemakers, cardiacresynchronization devices and defibrillators, utilize their leads forsensing electrocardiograms. The medical device may also have othersensors, such as an internal accelerometer and an external pressuresensor, which is external to the device yet still reside inside thepatient. The device can contain a power source such as a battery, acomputing hardware, a data storage unit such as electronic memory andcommunication hardware or related systems.

FIG. 1 presents an embodiment of an implantable medical device that maybe used to obtain ECG data without the use of leads. However, externalembodiments are contemplated by the invention. A monitor 10 is implantedsubcutaneously in the upper thoracic region of the patient's body 18near the patient's heart 16. The monitor 10 comprises a non-conductiveheader module 12 attached to a hermetically sealed enclosure 14. Theenclosure 14 contains the operating system of the monitor 10 and ispreferably conductive but can be covered in part by an electricallyinsulating coating. A first, subcutaneous, sensing electrode A is formedon the surface of the header module 12 and a second, subcutaneous,sensing electrode B is formed by an exposed portion of the enclosure 14.A feed-through extends through the mating surfaces of the header module12 and the enclosure 14 to electrically connect the first sensingelectrode A with the sensing circuitry (not shown) within the enclosure14, and the conductive sensing electrode B directly to the sensingcircuitry.

The electrical signals attendant to the depolarization andre-polarization of the heart 16 referred to as the ECG are sensed acrossthe sensing electrodes A and B. The monitor 10 is sutured tosubcutaneous tissue at a desired orientation for electrodes A and Brelative to the axis of the heart 16 to detect and record the ECG in asensing vector A-B for subsequent uplink telemetry transmission to anexternal programmer (not shown). FIG. 1 shows only one possibleorientation of the sensing electrodes A and B and sensing vector A-B. Itwill be understood by those of ordinary skill in the art that additionalorientations are possible. The hermetically sealed enclosure 14 includesa battery, circuitry that controls device operations and records ECGdata in memory registers, and a telemetry transceiver antenna ortransceiver electrodes and circuit that receives downlink telemetrycommands from and transmits stored data in a telemetry uplink to theexternal programmer. The circuitry and memory can be implemented indiscrete logic or a micro-computer based system with Analog/Digitalconversion of sampled ECG amplitude values.

As depicted in FIG. 2, an implantable medical device (IMD) 13 is amulti-chamber pacemaker that can both deliver electrical stimulation andmonitor potassium levels, as described in U.S. Patent Publication2006/0217771 A1, the contents of which are incorporated in theirentirety. The dual capability of IMD 13 is particularly well suited forpatients suffering from cardiac disease requiring pacing and concomitantkidney disease requiring monitoring of potassium concentrations fordialysis. The exemplary embodiment can deliver electric stimulation andrecord ECG data in the heart 15 of a patient. A right ventricular lead17 has an elongated insulated lead body carrying one or more concentriccoiled conductors separated from one another by tubular insulatedsheaths. The distal end of right ventricular lead 17 is deployed in theright ventricle 19 of heart 15. Located adjacent to the distal end ofthe lead body are one or more pacing/sensing electrodes 20, which areconfigured to deliver cardiac pacing and are further configured to sensedepolarizations of right ventricle 19. A fixation mechanism 22, such astines or a screw-in element anchors the distal ends in right ventricle19. The distal end also includes an elongated coil electrode 24configured to apply cardioversion or defibrillation therapy. Each of theelectrodes is coupled to one of the coiled conductors within the leadbody. At the proximal end of right ventricular lead 17 is a connector26, which couples the coiled conductors in the lead body to IMD 13 via aconnector module 28. A right atrial lead 30 includes an elongatedinsulated lead body carrying one or more concentric coiled conductorsseparated from one another by tubular insulated sheaths corresponding tothe structure of right ventricular lead 17. Located adjacent theJ-shaped distal end of right atrial lead 30 are one or morepacing/sensing electrodes 32, which are configured to sensedepolarizations and deliver pacing stimulations to right atrium 34.

Also shown in FIG. 2 is an elongated coil electrode 36 proximate to thedistal end of right atrial lead 30, and located in right atrium 34 andthe superior vena cava 38. At the proximal end of the lead is aconnector 40, which couples the coiled conductors in right atrial lead30 to IMD 13 via connector module 28. A coronary sinus lead 42 includesan elongated insulated lead body deployed in the great cardiac vein 44.The lead body carries one or more coiled conductors coupled to one ormore pacing/sensing electrodes 46. Electrodes 46 are configured todeliver ventricular pacing to left ventricle 48 and are furtherconfigured to sense depolarizations of left ventricle 48. Additionalpacing/sensing electrodes (not shown) may be deployed on coronary sinuslead 42 that are configured to pace and sense depolarizations of theleft atrium 50. At the proximal end of coronary sinus lead 42 isconnector 52, which couples the coiled conductors in coronary sinus lead42 to connector module 28. An exemplary electrode element 54A is coupledto the distal end of a lead 56. Lead 56 carries one or more conductorsseparated from one another by insulated sheaths. A connector 58 at theproximal end of the lead couples the conductors in lead 56 to IMD 13 viaconnector module 28. In addition to connector module 28, IMD 13 has ahousing 60 formed from one or more materials, including conductivematerials such as stainless steel or titanium. Housing 60 can includeinsulation, such as a coating of Parylene® (poly(p-xylylene)) orsilicone rubber, and in some variations, all or a portion of housing 60can be left uninsulated. The uninsulated portion of housing 60 can serveas a subcutaneous electrode and a return current path for electricalstimulations applied via other electrodes.

Also shown in FIG. 2 is electrode element 54A that includes twoelectrodes 62A and 62B. At least one of electrodes 62A and 62B isdeployed in or near test tissue and delivers stimulation to the tissue,while the other provides a return current path. The test tissue cancomprise a collection of autologous or non-autologous cells that aresensitive to [K⁺]. For example, the test tissue may be one of cardiacmuscle, skeletal muscle, smooth muscle, nerve tissue, skin, or the like.The IMD 13 includes a sensor that detects the electromechanical responseof the muscle to the stimulation delivered by electrodes 62A and 62B.The detected electromechanical response can include muscle tension,muscle strength, muscle density, muscle length and pressure generated bythe muscled. The electromechanical sensor can be incorporated completelywithin the housing of IMD 13 or can be present outside the housing.Example sensors include optical sensors for observing mechanicalresponses and an accelerometer that responds to muscle movement. Furtherembodiments of the sensor for detecting an electromechanical responseinclude pressure sensors and piezoelectric sensors.

In certain embodiments, the accelerometer can have a 3-axisaccelerometer capable of separately detecting heart and lung sounds ormovement and respiration rate. Heart and lung movement and respirationrate can indicate fluid volume overload. Any implantable device toobtaining ECG or other data can also have temperature sensingcapabilities.

FIG. 3 shows graphs of muscle force that illustrate exemplary techniquesto determine a concentration of [K⁺] in extracellular fluid (ECF) as afunction of the response of skeletal muscle to stimulations from anelectrode element such as electrode elements 54A. Each stimulus can havean amplitude of about 2 to about 20 Volts, for example, and a pulsewidth of about 0.1 to 1.0 milliseconds. Stimulus line 100 shows thetiming of stimuli delivered to the skeletal muscle via electrodes suchas electrodes 62A-B of FIG. 2. Response line 102 depicts a response ofskeletal muscle to the stimulations in an environment where [K⁺] is lowrelative to concentrations in intracellular fluid (ICF). In other words,response line 102 depicts a response of skeletal muscle in a “normal”patient. By contrast, response line 104 depicts a response of skeletalmuscle in a patient having elevated [K⁺].

The frequency of stimuli can vary from about 10 to about 150 Hz. Musclein a normal environment has longer duration contractions and can exhibitsome summation. Muscle contractions in a lower [K⁺] environment have alarger amplitude and have a longer duration than a high [K⁺]environment. As described in FIG. 3, data obtained from electricalstimulation of potassium-sensitive tissue can be used to supplement theanalysis of ECG data described herein.

Those skilled in the art will readily understand that the innovationsdisclosed here can readily be applied to data and electrical signals,including ECG data, obtained from non-implantable devices. For example,a plurality of electrodes can be placed on the skin of a subject. Theplurality of electrodes can connected to a medical device for measuringelectrical signals or a patch ECG device that transmits ECG by wirelesstelemetry to a receiver that can interpret the ECG data, such as theV-PATCH™ from VPMS Asia Pacific (Victoria, Australia). Electricalsignals related to heart or lung activity and/or ECG data, regardless ofsource, can be used in conjunction with the embodiments described below.

Processing Unit and Computational Procedure

The physiological signals obtained by the medical device of the presentinvention are processed by a processing unit. The processing unit can becomputing hardware that is disposed within the implantable medicaldevice or external to the device. Alternatively, the processing unit canbe external to the patient and receive the physiological data from theimplantable medical device and process the data either in real time orat a later time. A computational procedure, which can be referred to asthe forward computational procedure, is used to convert thephysiological signals into disease scores, which will be described belowin detail.

The processing unit can extract several details from each cardiac cycle.The complete cardiac cycle of the patient can be stored by the implantedmedical device or the processing unit and associated with a time index.In certain embodiments, not every cardiac cycle of the patient isrequired to be stored by the medical system and associated with a timeindex. For example, every other cardiac cycle or every nth integercardiac cycle can be processed. Alternatively, cardiac cycles thatoverlap certain time points can be analyzed since the time period ofcardiac cycles depends upon heart rate. In some embodiments, the timeindices of cardiac cycles indicate the chronological order of cardiaccycles, wherein adjacent time indexes are not restricted to immediatelyproximal cardiac cycles.

Table 1 lists various parameters or features that can be extracted fromthe ECG of each cardiac cycle. Each feature represents a scalar quantitythat describes a feature of the ECG of the cardiac cycles.

TABLE 1 Features extracted from the electrocardiogram Feature DefinitionF1 P-R interval F2 QRS width F3 Q-T interval or QT-dispersion F4 P-waveamplitude F5 P-wave peak F6 S-T segment depression F7 Inverted T-wavesF8 U-wave observation F9 T-wave peak amplitude F10 Heart RateVariability

The scalar values for features F1 through F10 have diverse magnitudesand units which complicate arriving at a combination of the featuresinto one or more risk scores that can be used to assess the potassiumstate of the patient. In particular, various features are typicallyreduced to a scalar quantity in the following units: P-R interval intime units, U-wave amplitude in potential units, S2 based upon acomparison with the feature QRS width in time units, Q-T interval intime units, P-wave amplitude in potential units per time unit, P-wavepeak in potential units, and T-wave amplitude in potential units. Otherfeatures are indicated by a yes/no observations such as depression ofS-T segment and inversion of the T-wave. Therefore, each of the featuresF1 through F10 can be converted to a value on a scale from 0 to 1 toallow direct comparison and or combination of features F1 through F10,which can herein be referred to as the common scale. Those skilled inthe art will understand that scales having other ranges can be used.

Table 2 shows various computational procedures that can be used toconvert the features F1 through F10 to the common scale. Computationalprocedures D1 through D3 are discrete mathematical equations that resultin an output of either 0 or 1. As shown in FIG. 4, computationalprocedure D1 indicates a value of 1 when a determinant or thresholdX_(c) is exceeded and otherwise indicates a value of 0. computationalprocedure D2 is similar except a value of 1 is indicated for a valueless than determinant or threshold X_(c). Computational procedure D3provides a value of 1 when the value deviates from a set point by anamount X_(c). The computational procedures S1, S2 and S3 are continuousmathematical functions with the possibility of any numerical valuebetween 0 and 1. Computational procedures D1, D2 and D3 have theadvantage of being easier to implement by a microprocessor because theyonly require a comparison of the argument X to a threshold value ofX_(C). However, the computational procedures D1, D2, and D3 do notprovide any proportional response to the input. Computational proceduresS1, S2 and S3 provide a more graded response, but impose a heaviercomputational burden on the microprocessor by either requiring amathematical computation shown in Table 2 or the use of a look-up table.However, both discrete and continuous computational procedures arecontemplated for use in the present invention. FIG. 5 presents exemplaryplots for computational procedures S1, S2 and S3.

TABLE 2 Computational procedures used for the conversion of the featuresinto scores Name Mathematical Expression D1${D_{1}\left( {x,x_{C}} \right)} = \left\{ \begin{matrix}{1,{x > x_{C}}} \\{0,{x \leq x_{C}}}\end{matrix} \right.$ D2${D_{2}\left( {x,x_{C}} \right)} = \left\{ \begin{matrix}{0,{x > x_{C}}} \\{1,{x \leq x_{C}}}\end{matrix} \right.$ D3${D_{3}\left( {x,x_{C}} \right)} = \left\{ \begin{matrix}{1,{{x} > x_{C}}} \\{0,{{x} \leq x_{C}}}\end{matrix} \right.$ S1${S_{1}\left( {x,x_{C},k} \right)} = \frac{1}{1 + e^{k{({x_{C} - x})}}}$S2${S_{2}\left( {x,x_{C},k} \right)} = \frac{1}{1 + e^{k{({x - x_{C}})}}}$S3${S_{3}\left( {x,x_{C},k} \right)} = {\frac{1}{1 + e^{k{({{\frac{3}{2}x_{C}} - x})}}} + \frac{1}{1 + e^{k{({x - \frac{x_{C}}{2}})}}}}$

In one embodiment, computational procedures D1 and S1 are designed toindicate that the value of a feature is increasing, where an increasedvalue is undesirable and will contribute to a disease risk scoreindicating an adverse condition. Computational procedures D2 and S2represent the reverse situation where a decreased value indicates acontribution to a disease risk score and an adverse condition.Computational procedures D3 and S3 produce high scores indicative of anadverse condition when the feature deviates from a central value eitherby increasing or by decreasing.

Below is an example illustrating the use of the features and theirconversion into raw scores using one of the discrete computationalprocedures D1 through D3. In this example, features F1 through F10 areas described in Table 1, and the value on the common scale are denotedwith P1 through P10. That is, the list below exemplifies one embodimentfor conversion of the scalar quantities for features F1 through F10 tovalue of 0 or 1 on the common scale using a computational procedureequivalent to one of D1 through D3.

If F1=P-R interval>200 msec, then P1=1, else P1=0;

If F2=QRS width>130 msec, then P2=1, else P2=0;

If F3=Q-T interval>220 msec, then P3=1, else P3=0 or if StandardDeviation of Q-T interval>20 msec, then P3=1, else P3=0;

If F4=P-wave amplitude<1 mV, then P4=1, else P4=0;

If F5=P-wave peak>1 mV/msec, then P5=1, else P5=0;

If F6=S-T segment depressed, then P6=1, else P6=0;

If F7=T-wave is inverted, then P7=1, else P7=0;

If F8=U-wave amplitude>2 mV, then P8=1, else P8=0;

If F9=T-wave peak amplitude>3 mV, then P9=1, else P9=0;

If F10=Heart Rate Variation (SDNN)<50 msec, then P10=1, else P10=0;

The correlation to the set of instructions described above can beexpressed using the discrete computational procedures D1, D2 or D3 tocompute the common scale values, which are shown below as P1 throughP10:

P1=D1 (F1, 200 msec);

P2=D1 (F2, 130 msec);

P3=D1 (F3, 220 msec);

P4=D2 (F4, 1 mV);

P5=D1 (F5, 1 mV/msec);

P6=D2 (F6, 1.1 mV);

P7=D2 (F7, 0);

P8=D1 (F8, 2 mV);

P9=D1 (F9, 3 mV);

P10=D2 (F10, 50 msec);

Similar expressions for the raw scores P1 through P10 can be writtenusing the continuous computational procedures S1 through S3 instead ofD1 through D3. While not presented herein, the use of expressions S1 toS3 to generate common scale values being any real value between 0 and 1is readily ascertainable by one having ordinary skill in the art uponapplying a determinant X_(c) and a factor k.

Afterwards, disease scores are calculated using the raw scores. Threeexamples are given below. In this case, DSL, DSH and DAR denote thedisease scores for hypokalemic, hyperkalemic and arrhythmic outcomesrespectively. Specifically, a higher value for DSL, DSH and DARindicates an increased prevalence of the respective condition. WL1, WL5,WH2, WA1, etc. denote weighting coefficients. The weighting coefficientscan be further refined as described below. In some embodiments, theweighting coefficients can be any number greater than or equal to zero.DSL=WL1*P1+WL6*P6+WL7*P7+WL8*P8+WL10*P10  (Eq. 1)DSH=WL2*P2+WL3*P3+WL4*P4+WL5*P5+WL9*P9+WL10*P10  (Eq. 2)DAR=WA1*P1+WA2*P2+ . . . +WA10*P10  (Eq. 3)

For the calculation of the disease scores, weighting coefficients aswell as the variables such as X_(C) and k values will need to bedetermined. For the remainder of the discussions, these variables,weighting coefficients, X_(C) and k, can be collectively denoted withthe symbol M. These constants can be predetermined and adjusted asneeded by the medical professionals attending the patient.Alternatively, the processing unit can adjust these constants based onthe patient outcomes. In some embodiments, the weighting coefficientsand value k can be set to 1, while the determinant value X_(C) is asdescribed above for each feature F1 through F10. That is, a diseasescore is calculated by a summation of individual weighed or non-weightedfeature scores as shown in Equation 4, wherein P_(k) is the featurescore and W_(k) is a weighting factor.Risk Score=Σ_(k=1) ^(n) W _(k) *P _(k),  (Eq. 4)

The flow chart for the overall forward computational procedure thatmonitors the patient is shown in FIG. 6 and outlined in steps below:

STEP 1: Record a cardiac cycle

STEP 2: Extract features F

STEP 3: Calculate raw scores P using features F and initial variablesfrom M

STEP 4: Calculate disease scores D using raw scores P and weightingcoefficients from M

STEP 5: If disease score>threshold for a period of time, issue alert

STEP 6: Go to step 1

Disease scores can be calculated for various conditions, including butnot limited to, hypokalemia, hyperkalemia, arrhythmias, hospitalizationsand acute heart failure.

FIG. 7 shows an example trace for a disease score. In that case, thedisease score exceeds the preset threshold of 2.5 at time index T=30,but subsequently returns back to a normal zone at time index T=34. Dueto its short duration, this event does not trigger a warning. However,the disease score again enters into the risk zone at time index of T=49,and this time, it remains there for longer than 10 times indicesresulting in the issuance of a warning. The selection of the thresholdvalues as well as the minimum duration of risk can be chosen by theclinician depending on the conditions of the patient or could bedetermined by a backward computational procedure as described herein.Furthermore, the time duration before a warning is issued can bedifferent for different disease scores. For example, for hyperkalemiatime durations can be much longer than those for hypokalemia.

In certain embodiments, the controller works to identify the variablesX_(c), k as well as the weighting coefficients, and the thresholds andthe time duration before a warning is issued, which are collectivelycalled M. This is accomplished using a backward computational procedurewherein operation in the overall system is shown in FIG. 8. The featureset F is fed into the forward computational procedures as describedabove. Afterwards, the resulting disease score is compared to the actualpatient outcome. The difference, called the error signal, is used toadjust the constant set M, which is used in the future execution of theforward computational procedures. For example, if the disease score andthe patient outcome are the same, then the error signal would be zero,indicating that there is no reason to alter the constants. On the otherhand, if there is discrepancy between the disease score and the actualpatient outcomes, then the error signal would be a non-zero value, whichin turn will drive the backward computational procedure to alter theconstant set M. The backward computational procedures can be constructedusing any of the many known statistical and signal processing methodssuch as the least squares and steepest descent.

Communication System

The communication system allows the transfer of data as well as thedisease scores and the variables from set M between the implantedmedical device and the external devices for monitoring the patient 401as shown in FIG. 9. In particular, the implanted medical device orexternal medical device 410 can be in wireless communication with alocal monitor 420 located in the vicinity of the patient. The localmonitor 420 can then communicate with either a local computer that canserve as a control processor for interpreting electrical signals fromthe patient, or the electrical signals from the patient can betransmitted to a remote control processor 430. In any scenario, aclinician 440 can monitor the control processor, provide the results ofclinical observations or lab tests, or adjust the set M used in diseasescore calculation or modify thresholds or time periods for generating analert. When an alert is generated as described below, the patient 401can be made aware through a signal (e.g. audio, visual, etc.) from localmonitor 420 and a clinician monitoring the control processor 430 can bemade aware of any patient having an alert.

The implanted medical device and/or the local monitor can share andtransmit data and instructions using any known method of wired orwireless telemetry. For example, a WMTS driver in any device can providean interface for communication via protocols, such as conventional RFranges allocated by Federal Communications Commission (FCC) for WirelessMedical Telemetry Service (WMTS). A 802.11 driver in any device cansupport an 802.11 wireless communication protocol such as 802.11a,802.11b, or 802.11g. Similarly, a Bluetooth driver can support RFcommunications according to the Bluetooth protocol. Any device can alsoinclude CDMA and GSM drivers for supporting cellular communicationsaccording to the code division multiple access (CDMA) protocol, or theGlobal System for Mobile Communications (GSM) protocol, respectively.Software Applications can invoke Network Protocols to make use of thesedrivers for communication with the local monitor 420 and/or the controlprocessor 430. Network Protocols in any device can implement a TCP/IPnetwork stack, for example, to support the Internet Protocol or othercommunication protocols. The preceding is merely exemplary of methods ofcommunication that can be used by an implanted medical device 410, thelocal monitor 420 or the remote control processor 430 wherein one ofordinary skill will understand that many ways of performing theobjectives of the invention are known within the art.

Those skilled in the art will readily understand that the communicationsystem can transmit other data in addition to the specific disease scoredata disclosed herein. Rather, many other patient parameters can beobserved with sensors or inputted to evaluate the dialytic status of thepatient, which can include both the effectiveness of dialysis treatmentin replacing natural kidney function or complications due to dialysistreatment, such as undesirable changes in potassium ion levels. Datathat can be collected and transmitted by the communication systeminclude, but is not limited to, 1) Non-potassium electrolytes andbiomarkers such as sodium and calcium; 2) metabolites such as urea,glucose and lactate; 3) hemodynamic parameters such as pulmonary arterypressure, left atrial pressure, right atrial pressure, left ventricularend diastolic pressure, O₂ saturation, and cardiac output; 4) serumbiomarkers such as creatinine, albumin, beta-2-microglobin and nGAL; 5)ECG parameters and features; 6) cardiac, skeletal contraction and/orlung data obtained from accelerometer sensors; and 7) values inputted bythe patient regarding physical condition.

As will be discussed in greater detail below, ECG parameters andfeatures can be used to calculate specific risk scores. However,additional data can be used to evaluate an overall dialytic clinicalrisk score (DCRS). The DCRS can be evaluated qualitatively by aphysician or a clinician to access the overall status of the patient. Inother embodiments, a DCRS can be calculated in an automated fashionusing an algorithm and the resulting information evaluable by aphysician or a clinician, where a monitoring physician or clinician canbe made aware of patients evaluated to have a DCRS that requires furtherevaluation in an automated fashion. That is, a change in DCRS can beused to trigger an automated alert for further evaluation by a physicianor clinician. The further exploration by a physician or clinician can beassisted by the division of data components between differentialdiagnostic dashboards, wherein the physician or clinician can bedirected to a specific diagnostic dashboard that contributed to thealert, for example, hyperkalemic, hyperglycemic, hypervolemic component,etc.

In certain embodiments, the DCRS does not need to include componentsfrom all data known about the patient. Rather, the DCRS can becalculated using a skip-logic method, wherein only certain parameterscontribute to the score based upon certain criteria. For example, themeasurement of a high pulse rate may trigger the calculation of DCRSbased upon certain additional parameters such as O₂ saturation,respiration rate, blood glucose, contractile strength (as measured byaccelerometer data), and electrolytes while excluding other parameters.As such, the basis for a DCRS score can change based upon specificpatient data. Still further, in certain embodiments ECG data and/orheart contractile strength data can provide an indication of sodium ionconcentration in the blood serum or in extracellular fluids.

As discussed above, FIG. 9 shows a communication system in accordancewith some embodiments where an implanted or external medical device 410can be in wireless communication with a local monitor 420 located in thevicinity of the patient that can relay data from the medical device 410to a remote process 430 and/or a clinician 440. FIG. 10 presentsadditional embodiments for the communication of data and otherinformation from and to a medical device including medical devices formonitoring an ECG or other electrical signals, including internal orexternal medical devices. The medical device can also include sensors orother medical devices for measuring any patient parameter including theparameters discussed above such as electrolytes, hemodynamic parameters,serum biomarkers, cardiac or skeletal muscle response and respiration,or patient-reported information.

In FIG. 10, a medical device 1000, which can be any of the medicaldevices or sensors discussed above, is in wireless communication with anaccess point 1057 can be a local monitoring device or a Wi-Fi router orother device that provides networking capabilities. The identity of theaccess point 1057 is not particularly limit and can include any devicecapable of relaying data such as smart phone or an iPad® device (notshown). The medical device 1000 through the access point 1057 cantransmit or receive data to or from a remote device via a computernetwork, pager network, cellular telecommunication network, and/orsatellite communication network, or via an RF link such as Bluetooth,WiFi, or MICS or as described in U.S. Pat. No. 5,683,432 “AdaptivePerformance-Optimizing Communication System for Communicating with anImplantable Medical Device” incorporated herein by reference in itsentirety, wherein there is no requirement for the electronic controllerto be implanted within the patient.

In certain embodiments, a telemetry circuit that enables programming ofthe medial device 1000 by means of a 2-way telemetry link. Uplinktelemetry allows device status and diagnostic/event data to be sent to aclinician or physician or another party for review to track thetreatment of a patient. Known telemetry systems suitable for use in thepractice of the present invention are contemplated by the invention.Such 2-way communication with the medical device 1000 is typically donevia a bi-directional radio-frequency telemetry link, such as theCareLink™ system (Medtronic, Inc., Minneapolis, Minn.). Further, ageneral purpose computer or any other device having computing power suchas a smart phone, iPad® or like device.

As shown in FIG. 10, in some embodiments, transmission of data to andfrom the medical device 1000 can be accomplished through a number ofdifferent external devices. Through the access device 1057, differenttypes of devices running applications for sending and receiving datafrom the medical device 1000 can be used, such as a desktop 1050 orlaptop PC 1051 or a cellular phone or smart phone device 1056. In someembodiments, data can be transmitted over the internet 1053 via a localrouter 1055 and/or modem 1054 for placement on a secure web server 1058and associated database 1059. The web server 1058 can be accessed by thepatient and/or a physician or clinician to receive or send data to themedical device 1000.

Various telemetry systems for providing the necessary communicationschannels between an electronic controller and a medical device have beendeveloped and are well known in the art, for example, telemetry systemssuitable for the present invention include U.S. Pat. No. 5,127,404,entitled “Telemetry Format for Implanted Medical Device”; U.S. Pat. No.4,374,382, entitled “Marker Channel Telemetry System for a MedicalDevice”; and U.S. Pat. No. 4,556,063 entitled “Telemetry System for aMedical Device,” which are all incorporated herein by reference. Inaddition to transmission over the internet, any device shown in FIG. 10can also directly share data with a 802.11 driver to support 802.11wireless communication protocol such as 802.11a, 802.11b, or 802.11g.Similarly, a Bluetooth driver can support RF communications according tothe Bluetooth protocol. Any device can also include CDMA and GSM driversfor supporting cellular communications according to the code divisionmultiple access (CDMA) protocol, or the Global System for MobileCommunications (GSM) protocol, respectively.

Disease Scoring

The process for calculating a disease risk score by the processor unitwill now be described with particularity. FIG. 11 presents a flowchartfor a process to monitor the real-time electrical signals of the body ofa subject that extracts values of different components from theelectrical signals including PR interval, QRS width, QT interval, P waveamplitude, P wave peak, ST segment, T waves, U wave amplitude, T wavepeak amplitude and heart rate variance corresponding to features F1through F10 as discussed above. The sequence of determining values forFeatures F1 through F10 can be different than presented in FIG. 11;however, FIG. 11 presents the order of feature determination from an ECGassociated with a particular time index in accordance with oneembodiment. In step 701, the processor unit determines the value of theP-R interval from an ECG of one cardiac cycle associated with a timeindex. In step 702, the processor unit determines the value of the QRSwidth from the ECG of one cardiac cycle associated with the time index.In step 703, the processor unit determines the value of the Q-T intervalfrom the ECG of one cardiac cycle associated with the time index. Instep 704, the processor unit determines the value of the P-waveamplitude from the ECG of one cardiac cycle associated with the timeindex. In step 705, the processor unit determines the value of theP-wave peak from the ECG of one cardiac cycle associated with the timeindex. In step 706, the processor unit determines the value of the S-Tsegment from the ECG of one cardiac cycle associated with the timeindex. In step 707, the processor unit determines T wave inversion fromthe ECG of one cardiac cycle associated with the time index. In step708, the processor unit determines the value of the U-wave amplitudefrom the ECG of one cardiac cycle associated with the time index. Instep 709, the processor unit determines the value of the T-wave peakfrom the ECG of one cardiac cycle associated with the time index. Instep 710, the processor unit determines the value of the heart ratevariance from the ECG of one cardiac cycle associated with the timeindex.

In FIG. 12, a process for transforming the values for features F1 to F10to scores on the common scale is shown. FIG. 10 shows the conversionperformed using one of the discrete computational procedures D1 throughD3 as described above. Using computational procedures D1 through D3, adeterminant X_(c) within set M must be determined. As described above,Xc can be set to initial values within set M or can be refined values asdetermined by application of the backwards computational procedure. Inadditional embodiments, the set M can include a value k to allow for useof one of the continuous computational procedures S1 through S3, asdescribed above, for generation of one or more of the common scalevalues P1 through P10. In any scenario, the values for features F1through F10 can be used to generate values P1 though P10 on the commonscale provided that at least a determinant X_(c) is set in set M foreach of features F1 through F10. That is, each feature F1 through F10 iscompared to a determinate X_(c) for a specific feature, which can bedenoted X_(c)(F1), X_(c)(F2), X_(c)(F3), X_(c)(F4) . . . X_(c)(F10).Similarly, the value k associated with any specific feature can bereferenced by similar nomenclature: k(F1), k(F2), k(F3), k(F4) . . . Xc(F10).

In step 1202 in FIG. 12, the value F1 for the P-R interval is comparedto determinant X_(c)(F1) to set the value of S1 to 0 or to set the valueof S1 to 0 using a discrete computational procedure. In someembodiments, the determinant X_(c)(F1) has a value of 200 msec. In step1205, the value F2 for QRS width is compared to determinant X_(c)(F2) toset the value of S1 to 0 or to set the value of S1 to 0 using a discretecomputational procedure. In some embodiments, the determinant X_(c)(F2)has a value of 130 msec. In step 1215, the value F3 for Q-T interval iscompared to determinant X_(c)(F3) to set the value of S1 to 0 or to setthe value of S1 to 0 using a discrete computational procedure. In someembodiments, the determinant X_(c)(F3) has a value of 220 msec. In step815, the value F4 for P-wave amplitude is compared to determinantX_(c)(F4) to set the value of S1 to 0 or to set the value of S1 to 0using a discrete computational procedure. In some embodiments, thedeterminant X_(c)(F4) has a value of 1 mV. In step 820, the value F4 forP-wave amplitude is compared to determinant X_(c)(F4) to set the valueof S1 to 0 or to set the value of S1 to 0 using a discrete computationalprocedure. In some embodiments, the determinant X_(c)(F4) has a value of1 mV. In step 1220, the value F5 for P-wave peak is compared todeterminant X_(c)(F5) to set the value of S1 to 0 or to set the value ofS1 to 0 using a discrete computational procedures. In some embodiments,the determinant X_(c)(F5) has a value of 1 mV msec⁻¹. In step 1225, thevalue F6 for S-T segment is compared to determinant X_(c)(F6) to set thevalue of S1 to 0 or to set the value of S1 to 0 using a discretecomputational procedure. In some embodiments, the determinant X_(c)(F6)is a yes or no determination of whether S-T segment is depressed. Instep 1230, the value F7 for T-wave inversion is compared to determinantX_(c)(F7) to set the value of S1 to 0 or to set the value of S1 to 0using a discrete computational procedure. In some embodiments, thedeterminant X_(c)(F7) is a yes or no determination of whether the T-waveis inverted. In step 1235, the value F8 for U-wave amplitude is comparedto determinant X_(c)(F8) to set the value of S1 to 0 or to set the valueof S1 to 0 using a discrete computational procedure. In someembodiments, the determinant X_(c)(F8) has a value of 2 mV. In step1240, the value F9 for T-wave peak is compared to determinant X_(c)(F9)to set the value of S1 to 0 or to set the value of S1 to 0 using adiscrete computational procedure. In some embodiments, the determinantX_(c)(F9) has a value of 1 msec. In step 1245, the value F10 for heartrate variability is compared to determinant X_(c)(F10) to set the valueof S1 to 0 or to set the value of S1 to 0 using a discrete computationalprocedure. In some embodiments, the determinant X_(c)(F10) has a valueof 50 msec.

After the assignment of all set values, a DSL disease score iscalculated for the time index using Eq. 1 described above. In someembodiments, the weighting coefficients WL1, WL2, etc. are set to 1. Inother embodiments, the weighting coefficients WL1, WL2, etc. are set toa value found in the current set M. Similarly, a DSH disease score iscalculated for the time index using Eq. 2 described above. In someembodiments, the weighting coefficients WL1, WL2, etc. are set to 1. Inother embodiments, the weighting coefficients WL1, WL2, etc. are set toa value found in the current set M. Further a, a DAR disease score iscalculated for the time index using Eq. 3 described above. In someembodiments, the weighting coefficients WA1, WA2, etc. are set to 1. Inother embodiments, the weighting coefficients WA1, WA2, etc. are set toa value found in the current set M.

The DSL disease score calculated by Eq. 1 indicates the presence of ahypokalemia condition and the DSH disease score calculated by Eq. 2indicates the presence of a hyperkalemia condition. The presence ofhypokalemia condition and hyperkalemia condition are mutually exclusive.As such, in some embodiments the processor unit is configured to issue awarning for hypokalemia if requisite conditions are satisfied prior toissuing a warning for hyperkalemia if requisite conditions aresatisfied.

FIG. 13 shows an embodiment for determining if conditions are satisfiedfor issuing an alert for hyperkalemia or hypokalemia. In FIG. 13, awarning is issued if a DSL risk score or a DSH disease score exceeds athreshold for a set number of consecutive time indices. The thresholdfor DSL disease score or DSH disease score can be separately set and canbe refined as part of set M with the backward computational procedures.As explained above, a DSL disease score and DSH disease score are setfor each time index. The time period between adjacent time indices isknown by the processor unit. As such, a certain set of contiguous timeindices can be associated with a specific time period by the processorunit.

In step 901, the DSL disease value for a time index is compared to athreshold for DSL disease score. If the threshold is exceeded, a counterfor DSL disease score (C_DSL) is incremented by an integer value of 1.If the threshold is not exceeded, then the counter C_DSL is reset to 0.In step 905, the current count of the counter for DSL disease score(C_DSL) is compared to an alert time period which can be indicated bythe C_DSL exceeding a safe value CS_DSL. For example, if the alert timeperiod is 5 minutes and 15 seconds separate adjacent time indices, thenthe safe value CS_DSL for the counter can be set to 20, where an alertfor hypokalemia is issued in step 905 if C_DSL exceeds CS_DSL. In step910, the current count of the counter for DSH disease score (C_DSH) isincremented by an integer value 1 if the threshold for DSH disease scoreis exceeded. If the threshold for DSH disease score is not exceeded fora time index, then the counter C_DSH is reset to 0. In step 915, thecurrent count of the C_DSH counter is compared to a safe value CS_DSH.An alert for hyperkalemia is issued in step 915 if the counter C_DSHexceeds CS_DSH.

Those skilled in the art will readily understand that the steps shown inFIG. 12 represent one embodiment for determining if a DSL disease scoreand/or DSH disease score exceed a threshold value for a significantperiod to warrant that an appropriate alert be issued. Those skilled inthe art will readily recognize that whether the DSL disease score or DSHdisease score exceeds a threshold a sufficient number of times or for asufficient period of time can be evaluated by additional or alternativemeans without departing from the invention. As an example, step 901 canbe modified such that the counter for DSL disease score (C_DSL) is notreset upon evaluation of a time index that does not have a DSL diseasescore below the threshold. As an alternative, the C_DSL counter can beset to zero if a certain prior number of time indices or time indicescorresponding to a set period of time fall below the threshold for theDSL disease score. For example, the processor unit can be instructed toreset C_DSL to zero if all of the time indices from the last 3 minutes(or another appropriate time period) were below the threshold. As such,the observation of only a few time indices below the threshold will notreset the counter C_DSL nor increment the counter C_DSL by an integervalue of 1; rather, the count value of C_DSL can be left unchanged untilthe DSL disease score is observed below a threshold for an interveningperiod of time. As such, the decision to issue an alert for hypokalemiain step 905 can be based upon a moving average time frame for a numberof time indices that exceed the threshold value during a defined timewindow.

Step 910 for determining a count for C_DSH can be modified in the samemanner as for C_DSL in step 901. Further, a counter for the DAR disease(C_DAR) score exceeding a threshold can be established in the samemanner as for C_DSL and C_DSH with parallel protocols for deciding whenthe C_DAR has reached a requisite level to issue an alert forarrhythmia.

Those skilled in the art will understand that the threshold to which anyof the described risk scores are compared to for the purposes of issuingan alert, as for example as in FIG. 12, is not required to be a fixedvalue. In some embodiments, the threshold can be a fixed value, which,for example, can be correlated to specific levels of potassium ions orother electrolytes. In other embodiment, the threshold can vary and canbe recalculated during the course of monitoring of a patient. Forexample, the system can observe an average risk score for the patientover a period of time or a time window to establish a baseline riskscore value. In some instances, the baseline risk score value can beestablished during a period defined by a patient user and/or aclinician. In other instances, the baseline risk score value can bedetermined periodically by calculating an average risk score during aperiod of time where no alarms or adverse conditions are reported. Insome embodiments, the baseline risk score can be established over aperiod from 3 hours to about 2 weeks. In other embodiments, the baselinerisk score can be established over a period from about 3 hours to about1 week, from about 1 week to about 2 weeks, from about 3 days to about 2weeks, from about 3 days to about 1 week or from about 1 day to about 2weeks.

Once a baseline risk score for a patient is established, the thresholdfor any risk score described herein can be calculated based upon thebaseline risk score. As discussed above, when a risk score (e.g. DSL,DSH, DAR) exceeds a threshold for the risk score, then a counter for therespective risk score (e.g. C_DSL, C_DSH, C_DAR) advances and an alertcan be issued when the counter value exceeds a limit. The threshold towhich a risk score is compared for purposes of advancing thecorresponding counter can be a floating value that changes based uponthe determined baseline risk score. In some embodiments, the thresholdcan be set at a value that is a certain percentage greater than thebaseline risk score. In one embodiment, a threshold for a risk score canbe any of from about 10 to about 100%, from about 15 to about 50%, formabout 15 to about 40%, from about 20% to about 60% or from about 25% toabout 50% greater than the determined baseline risk score. In otherembodiments, a threshold for a risk score can be set as a specificabsolute value over the determined baseline risk score.

Since the baseline risk score for each risk score DSL, DSH and DAR canbe adjusted, a patient can be evaluated as being at risk as a result ofa relative change in risk score since the last time the baseline riskscore was calculated. As such, baseline risk scores and thresholds canaccount for patient-to-patient variability as well as gradual changes inpatient ECG parameters that do not represent a greater susceptibility tohyperkalemia/hypokalemia or arrhythmias. That is, it is possible for thebaseline risk score of patients to change overtime due to benign causesthat do not represent an increased risk for hyperkalemia/hypokalemia orarrhythmias, where such changes are gradual over time. As describedabove, the system can account for such drift in baseline risk score,where an alarm is only triggered in response to a significant increasein risk score over a relatively short period of time rather than basedupon an absolute risk score value.

Backward Computational Procedure

In FIG. 8, the features set to the common scale are provided in 501 foroperation on by the forward computational procedure 505. As describedabove, the forward computational procedure is any of Equations 1 through3 to calculate a disease risk score DSL, DSH and/or DAR. In step 510,periodic clinical data regarding measured patient condition can besupplied to the system. For example, information regarding serumpotassium obtained from standard laboratory tests can periodically beinputted to the control processor and compared to the disease risk scoregenerated at the time serum potassium was measured.

The threshold set for the disease risk score is correlated with anexpected potassium serum level. A discrepancy between disease risk scoreand the clinical data from step 510 can result in an error value whichis produced by the summation step (“sigma”) in step 515. When an erroris detected in step 515, the backward computational procedure can beapplied in step 520 to adjust the set of weight, determinant (X_(c))and/or k values in the set M used by the forward computational procedureto generate risk scores. The new set M can be used in the forwardcomputational procedure in step 505 going forward to refine the set M inan iterative fashion.

Each of Equations 1 through 3 is a linear combination of the product ofa weighting factor and a feature value (P) on the common scale.Refinement of determinant X_(c) and/or k value will lead to a change inthe feature value (P) that will modify the calculated disease score.Likewise, modification of the weighting factors will modify thecalculated disease score. A disease score such as DSL in Equation 1 is alinear summation of 5 product terms. Linear functions and computationalprocedures are susceptible to refinement by known statistical techniquessuch as least squares regression fit and steepest descent. Suchstatistical techniques typical require the observation of more datapoints than the number of variable to be refined for an accuraterefinement. In least square refinement, variables are brought to a stateof best fit with the number of observations by reducing the value of thesum of squares of residuals, where the residuals are the distance from abest fit value and an observed value. Here, the summation of the squaresof residuals between the calculated disease risk score calculated withrefined set M and the observed potassium serum level can be performed.

In some embodiments, the backward computational procedures to refine setM is only applied to refining one of the weighting factors, thedeterminant X_(c) or the value k. In other embodiments, each ofweighting factors, the determinants X_(c) and the values k areseparately refined to generate separate sets M. That is, for example,weighting factors are refined without modifying determinants X_(c) andthe values k; determinants X_(c) are refined without modifying weightingfactors and the values k; and the values k are refined without modifyingthe determinants X_(c) and the weighting factors. The refined set Mhaving the best fit can be maintained and carried forward to step 505.

In some embodiments, the amount of refinement can be restrained toprevent over refinement or refinement error. In some embodiments, theamount of refinement to the determinants Xc can be restrained. Forexample, the amount that determinants Xc can be modified from theirinitial values can be limited to one of about 30% or less, about 25% orless, about 20% or less, about 15% or less, about 10% or less or about5% or less. Similarly, the amount of the weighting factors can berestrained to not exceed a certain value. In some embodiments, theweighting factor can be limited to not exceed one or more from about2.5, about 2 and about 1.5.

Chronic Monitoring of Electrolytes and pH

A patient can be monitored in a chronic fashion for changes inelectrolytes in addition of potassium ion or in a manner to supplementmonitoring by ECG data only. Similarly, the patient can be monitored forchanges in pH.

One goal of hemodialysis, ultrafiltration, and like treatments is toensure that the patient's blood pH and electrolyte concentrations arewithin acceptable ranges. Typical ranges of pH and blood electrolyteconcentration that are desired during or following a blood fluid removalsession are provided in Table 3 below. As indicated in Table 3,concentrations of various acids or bases (or salts or hydrates thereof)are often important in determining the pH of blood. Accordingly, sometypical target concentrations of such acids or bases are presented inTable 3.

TABLE 3 Typical target ranges for pH and electrolytes (ref. MedicalSurgical Nursing, 7^(th) Ed., 2007) Target Range pH 7.35-7.45 Phosphate2.8-4.5 mg/dL Bicarbonate 22-26 mEq/L Cl⁻ 96-106 mEq/L Mg²⁺ 1.5-2.5mEq/L Na⁺ 135-145 mEq/L K⁺ 3.5-5.0 mEq/L Ca²⁺ 4.5-5.5 mEq/L

In hemodialysis sessions, a patient's blood is dialyzed against adialysate through an artificial dialysis membrane or using theperitoneal membrane in the case of peritoneal dialysis. The dialysatecan also serve as a replacement fluid where ultrafiltration is performedto remove fluid from the blood. Suitable components that may be used indialysate or replacement fluid include bicarbonate, acetate, lactate,citrate, amino acid and protein buffers. The concentration andcomposition of the buffers and components thereof may be adjusted basedon monitored pH of the patient's blood. Similarly, the concentration ofelectrolytes such as sodium, potassium, calcium, and chloride inreplacement fluid or dialysate may be set or altered based the monitoredlevels of electrolytes.

The methods, systems and devices described herein may be used, in someembodiments, to set the initial electrolyte concentration and pH (buffercomponents and concentration) based on monitoring that occurs before ablood fluid removal or dialysis session starts, herein referred to as ablood fluid removal session. In some embodiments, the monitoring ischronic; e.g., monitoring is performed intermittently, periodically orcontinuously over the course of days, weeks, months or years. In anattempt to minimize interference with the patient's lifestyle, themonitoring system, or components thereof, can be implantable or wearablesimilar to the devices described above.

In some embodiments, one or more sensors are employed to detect one ormore ions to gauge pH or electrolytes in the blood. In some embodiments,a sensor can have more than one transducer, even if leadless, that conmonitor more than one ionic species. By measuring more than one ionicspecies, a more detailed understanding of the levels of variouselectrolytes or blood components may be had. For example, in somepatients in some situations, one electrolyte may be at elevated levelswhile another may be at reduced levels. In some embodiments, more thanone sensor for the same ion is employed for purposes of resultconfirmation and redundancy, which can improve reliability and accuracy.In some embodiments, sensors for the same ion may be configured toaccurately detect different ranges of concentrations of the ion. Inembodiments, more than one transducer is present in a single unit. Thisallows for convenient data collection and circuitry, as all the data maybe collected in one place at the same time. Further, the multipletransducers may share the same fluid collection mechanism (e.g., amicrodialyzer in the case of an implant), and if needed or desired, mayshare the same data processing and memory storage components.

Sensor that measure pH or electrolytes by direct contact with bodilyfluids can be employed, such as ion-selective electrodes. Similarly,pacemakers or external or implantable ECG monitors (such as the Reveal®system) can be used to monitor electrolytes and can optionally be usedin conjunction with sensor that take measurements through direct contactwith bodily fluids.

Implantable sensors or sensors in which the transducer is chronicallyinserted in a tissue or blood of a patient may be calibrated prior toimplant by placement of the transducer in blood (or other conditionsmimicking the implant environment) with known pH or electrolyteconcentrations. The sensors can be recalibrated while implanted in thepatients. For example, blood pH and electrolyte concentration can bemeasured external to the patient, e.g., via blood draws, and results ofthe external monitoring can be communicated to the implanted sensor byreceiving input, e.g., from healthcare providers. Thus, the sensor, ifsensor has necessary electronics, can recalibrate based on the inputregarding the external measurements. Alternatively, or in addition, thesensor may have an internal reference built in, such as with theMedtronic, Inc. Bravo® pH sensor. Alternatively, in cases where thesensor outputs raw data to an external device, the external device maybe calibrated to interpret the raw data from the sensor with regard toinput regarding the external measurements.

Referring now to FIG. 14, the depicted method includes identifying,selecting or diagnosing a patient for which a blood fluid removal ordialysis session is indicated 800 and monitoring pH or electrolytelevels of the blood of the patient 810. The monitoring 810 can bechronic and may employ one or more implantable sensors or an ECGmonitoring device. Based on the monitored pH or electrolyteconcentration, the fluid (e.g., dialysate or replacement fluid)composition (e.g., electrolyte concentration, buffer composition andconcentration) for use initial use in a blood fluid removal session maybe set 820. As described above, the ability to chronically monitor pH orelectrolyte concentrations of the patient's blood provides the abilityto tailor the fluid composition prior to each blood fluid removalsession, as opposed to current standard practice in which the fluidcomposition is adjusted on a monthly basis (or thereabout). As multipleblood fluid removal sessions (e.g., two to three a week) may occur witha month, setting the fluid composition on a monthly basis may result inthe patient undergoing several blood fluid removal sessions with a fluidcomposition that may no longer be well suited for the patient.

Referring now to FIG. 15, method includes identifying, selecting ordiagnosing a patient for which a blood fluid removal or dialysis sessionis indicated 800 and monitoring pH or electrolyte levels of the blood ofthe patient 810. As with the method in FIG. 14, the monitoring 810 maybe chronic and may employ one or more implantable sensors or an ECGmonitoring device. The method depicted in FIG. 16 includes determiningwhether the pH or electrolyte concentration is out of range 830 based ondata acquired during the monitoring 810. For example, a determination830 can be made as to whether pH or electrolyte levels crossed athreshold (e.g., a ceiling or floor). Suitable thresholds or ranges maybe stored in, for example, a look-up table in memory of a sensor device,a blood fluid removal device, or other suitable device for purposes ofdetermining whether the pH or electrolyte concentration is out of range830 based on data acquired during the monitoring. If the pH orelectrolytes are determined to be within range, monitoring 810 maycontinue. If the pH or electrolytes are determined to be out of range(e.g., cross a threshold), an alert 840 can be issued or a blood fluidremoval session (850) may be scheduled.

The scheduled blood fluid removal session may take into account themonitored 810 pH or electrolytes, e.g. as described with regard to FIG.14. The scheduling may occur automatically, e.g. the sensor or a devicein communication with the sensor may transmit data and cause schedulingof session over internet, telephone, or other suitable network, or usingany of the communication systems described above.

Any suitable alert 840 may be issued. The alert may be a tactile cue,such as vibration or audible alarm, generated by a sensor or a device incommunication with sensor. The alert may provide the patient with noticethat medical attention should be sought. The alert may also provideinformation to a healthcare provider regarding the nature of the healthissue (e.g., pH or electrolytes out of range) and treatment (e.g., bloodfluid removal session) for which the alert 840 was issued. The sensor ora device in communication with the sensor may alert the healthcareprovider by transmitting the alert or related information over theinternet, a telephone network, or other suitable network to a device incommunication with the healthcare provider.

Referring now to FIG. 16, the depicted method includes identifying,selecting or diagnosing a patient for which a blood fluid removal ordialysis session is indicated 800 and monitoring pH or electrolytelevels of the blood of the patient 810. The monitoring 810 can bechronic and may employ one or more implantable sensors or an internal orexternal ECG measuring device. Based on the monitored pH or electrolyteconcentration, the rate of flow of dialysate or blood, based in part onthe concentration of electrolytes and pH composition of the dialysate,is set 901. As described above, the rate of flow of dialysate or bloodaffects the rate of transfer of electrolytes, etc. across the dialysismembrane. Accordingly, depending on the composition of the dialysateused, the rate of flow of the dialysate or blood may be adjusted or setso that desirable blood pH and electrolyte levels may be achieved duringthe course of a treatment session.

In additionally embodiments, the one or more sensors used to monitor pHand/or electrolytes described above can be used to modify thecomposition of a dialysate or a replacement fluid during dialysis.Referring now to FIG. 17, the depicted method includes initiating ablood fluid removal or dialysis session 801 and monitoring pH orelectrolyte concentration of blood 810. As discussed above, themonitoring may occur via one or more implanted sensors or an internal orexternal ECG measuring device. Based on the monitored pH orelectrolytes, the pH or electrolyte composition or concentration offluid (e.g., dialysate or replacement fluid) used in the blood fluidremoval session may be adjusted 860. For example, based one or more ofthe current value of a monitored ionic species or the rate of change inthe monitored ionic species, the fluid composition may be adjusted, e.g.as discussed above.

Referring now to FIG. 18, the depicted method show a method where bloodelectrolyte concentration or pH is adjusted by altering the flow rate ofdialysate or blood. The method includes initiating a blood fluid removalsession 801, such as a hemodialysis session, and monitoring an indicatorof pH or electrolyte 810, which can be in the patient, upstream of thedevice, downstream of the device, within the device, or the like. Basedon the monitored data (810), adjustments to the flow of dialysate orblood may be made 900 to adjust the electrolyte concentration or pH inthe blood that gets returned to the patient.

Automated Updating of Dialysis Parameters

In certain embodiments, the monitoring of patient electrolytes or pH, asdescribed above, between dialysis treatment sessions can be used toassist in determining the appropriate scheduling or length of a futuredialysis session and/or an appropriate dialysate or replacement solutionto be used in such a session. By comparing the patient's past responsesto dialysis parameters or changes in dialysis parameters, a system canbe able to avoid future use of parameters that may harm the patient andcan learn which parameters are likely to be most effective in treatingthe patient in a blood fluid removal or dialysis session. Dialysisparameters include scheduling, length of dialysis sessions as well asdialysate or replacement fluid composition, which are referred to assystem parameters herein.

Referring to FIG. 19, a high level schematic overview of embodiments ofthe present disclosure is shown. As shown in FIG. 19, a learningalgorithm 520 is employed to determine what system parameters work wellto produce desired patient physiological results based on input. Anysuitable input variable 500 can be considered by the algorithm 520 inthe learning process. For example, variables such as how long it hasbeen since the patient's last blood removal session may be input. Suchinput could be important as patients undergoing, for example,hemodialysis on a Monday, Wednesday, Friday schedule are more likely tosuffer an adverse cardiac event just before, during or after the Mondayblood fluid removal session. Accordingly, the algorithm 520 may considerwhether a different set of system parameters should be employed when thepatient has not undergone a session in 72 hours relative to when thepatient has not undergone a session in 48 hours. Input variables 500 mayalso include whether the patient has limited time to undergo a bloodfluid removal session. The algorithm 520 can determine whether a fasterfluid removal rate should be used or whether a partial session at areduced fluid removal rate would likely be more effective based on thepatient's history of response to fast fluid removal rates.Alternatively, the patient may have additional time to undergo a bloodfluid removal session, and the algorithm 520 can take such input 500into account to determine whether there may be an advantage to slowerfluid removal rates or slower adjustment of a concentration of anelectrolyte based on the patient's history. Of course, it will beunderstood that any other suitable input variables 500 may be enteredregarding target outcomes (e.g., quick session, long session, etc.),patient history (e.g., time since last session), or the like. Inembodiments, input that takes into account future patient behavior orneeds may be entered into the system. For example, if a patient knowsthat they will miss a session or the time until their next session willbe delayed from normal, time until next session may be entered, whichmay affect the system parameters (e.g., may remove additional fluid,etc.). By way of another example, if the patient knows that they willeat or drink an amount more than optimal before the session, expectedconsumption levels may be input in the system.

As shown in FIG. 19, the algorithm 520, based on input variables 500,and patient physiological variables 510 may determine appropriate systemvariables 530 to employ based on the patient's history with blood fluidsessions under the algorithm. During a blood fluid session, systemvariables 530 may be changed and the patient physiological response maybe monitored in response to the changed system variables. If one or moreof the patient's physiological variables 510 improve, the algorithm 530can associate the changed system variables 530 with the improved patientoutcome so that the changed system variables 530 may be used later inthe session or in a future session when the patient has a similar set ofphysiological variables 510. If one or more of the patient'sphysiological variables 510 worsen, the algorithm 530 can associate thechanged system variables 530 with a worsened patient outcome so that thechanged system variables 530 may be avoided later in the session or in afuture session when the patient has a similar set of physiologicalvariables 510.

In embodiments, the input variables 500 include patient physiologicalvariables that have occurred in a time period preceding a blood fluidremoval session. For example, the time period may be a period of time(e.g., all or one or more portions of time) since the patient's lastsession. In embodiments, the input variables include input indicating(i) how long favorable patient variables 510 (e.g., above or below apredetermined threshold) were observed after the last session; (ii) therate of change of patient variables 510 following the last session,(iii) etc., all of which may be compared against system parameters 530used in the previous session. If the patient physiological 510 or othervariables (e.g. patient input regarding how the patient has felt), werefavorable since the last session, the system may employ similarvariables in future sessions. It may also or alternatively be desirableto monitor patient physiological or other variables in a time periodleading up to a session and input such variables into the algorithm 520or system before the session. The system or algorithm 520 can thendetermine whether the patient has presented with similar symptoms orparameters in previous sessions and employ system variables 530 to whichthe patient responded favorably, either in the session, after thesession, or both in the session and after the session. Accordingly, thesystem or algorithm 520 may monitor patient well-being, which may bederived from patient physiological variable 510 or input variables 500,within a session and between sessions to determine which systemvariables should be employed and changed based on the patient responseto previous sessions. As indicated by the dashed lines and arrows inFIG. 19, patient physiological variables 510 obtained between sessionsand system variables 530 used in a prior session may be input variables500 in a current or upcoming session.

In embodiments, the physiological variables 510 are monitored by sensorsthat feed data regarding the variables directly into the algorithm 520or electronics running the algorithm. The sensors may monitor fluidvolume in the patient's blood; fluid volume in the patient's tissue;concentrations of electrolytes in the patient's blood; pH of thepatient's blood; one or more cardiovascular parameter of the patient,such as blood pressure, heart rhythm, heart rate; or combinations orindicators thereof. The sensors may monitor the patient physiologicalparameters before, during or after a blood fluid removal session.

A sensor configured to monitor hemoglobin levels may also be used as anindicator of blood fluid volume, as hemoglobin concentration istypically proportional to red blood cell concentration. Thus, lower thehemoglobin concentrations may be indicative of higher blood fluidvolume. Any suitable sensor may be used to measure hemoglobinconcentration, such as sensors used in pulse oximeters which measureadsorption of red and infrared light to determine concentration ofoxygenated hemoglobin and deoxyhemoglobin, respectfully. The sensors(which may include the associated light source(s)) may be placed in anysuitable location, such as around tubing that carries blood from thepatient to the blood fluid removal device or from the blood fluidremoval device to the patient, within the blood fluid removal device, orthe like. In addition or alternatively, a sensor may be implanted in apatient and disposed about a blood vessel to measure hemoglobin levels,and thus hematocrit and blood fluid levels. By way of further example,total blood protein or albumin concentrations and blood pressure, aloneor in combination, can be used to evaluate blood volume. High bloodpressure combined with low hematocrit or low blood protein may indicatea higher possibility of blood fluid overloading. Alternatively oradditionally, blood viscosity may be used as an indicator of blood fluidvolume and may be measured by pressure or flow. Impedance, capacitance,or dialectic constant sensors may be employed to monitor fluid volume.For example, impedance may be monitored between two electrodes. Theelectrodes may be operably coupled to control and processing electronicsvia leads. The electronics are configured to generate a voltagedifferential between the electrodes, current may be measured, andimpedance calculated. The measurement may be done in either DC or ACmode. Impedance or phase angle may be correlated to tissue fluid volume.Tissue impedance sensing for purposes of monitoring tissue fluid volumehas been well documented. One example of a well studied system that maybe used or modified for use herein is Medtronic, Inc.'s OptiVol® fluidstatus monitoring system. Such a system, or other similar systems, havewell-documented procedures for determining acceptable ranges of tissueimpedance and thus fluid volume. See, e.g., (i) Siegenthalar, et al.Journal of Clinical Monitoring and Computing (2010): 24:449-451, and(ii) Wang, Am. J. Cardiology, 99(Suppl):3G-1-G, May 21, 2007.Alternatively or in addition, tissue impedance may be monitored for asuitable period of time to establish as suitable baseline, and patientmarkers or clinician input may be used to instruct whether the patientis fluid overloaded or under-loaded. The data acquired by impedancesensor and input data regarding fluid status of the patient at the timethe sensor data is acquired may be used to establish suitable ranges forimpedance values.

Suitable transducers may include an ion selective electrode configuredto detect H⁺ ions, K⁺ ions, Na⁺ ions, Ca²⁺ ions, Cl⁻ ions, phosphateions, magnesium ions, acetate ions, amino acids ions, or the like. Suchelectrodes, and components of sensors employing such electrodes, areknown in the art and may be employed, or modified to be employed, foruse in the monitoring described herein. One or more sensors may beemployed to detect one or more ions to gauge pH or electrolytes in theblood. In some embodiments, a sensor may have more than one transducer,even if leadless, that may monitor more than one ionic species. Bymeasuring more than one ionic species, a more detailed understanding ofthe levels of various electrolytes or blood components may be had. Forexample, in some patients in some situations, one electrolyte may be atelevated levels while another may be at reduced levels. In someembodiments, more than one sensor for the same ion is employed forpurposes of result confirmation and redundancy, which can improvereliability and accuracy. In some embodiments, sensors for the same ionmay be configured to accurately detect different ranges ofconcentrations of the ion. In embodiments, more than one transducer ispresent in a single unit. This allows for convenient data collection andcircuitry, as all the data may be collected in one place at the sametime. Further, the multiple transducers may share the same fluidcollection mechanism (e.g., a microdialyzer in the case of an implant),and if needed or desired, may share the same data processing and memorystorage components. A sensor (or transducer) for detecting pH,electrolyte concentration, or the like may be placed at any suitablelocation for purposes of monitoring electrolytes or pH. For example, thesensor may be implanted in the patient, located external to the patientan upstream of a blood fluid removal device, located external to thepatient and downstream of the blood fluid removal device, or the like.

One suitable implantable sensor device that is configured to monitor apatient's ECG signals is a Medtronic, Inc.'s Reveal® series insertablecardiac monitor described above. In embodiments, the sensor device maybe a suitably equipped pacemaker or defibrillator already implanted inthe patient. Monitored cardiac signals from such a device may betransmitted to a blood fluid removal device or intermediate device foruse in the blood fluid removal session or for setting the prescriptionfor the blood fluid removal session. Blood pressure monitors, which maybe external or implantable (such as Medtronic Inc.'s active leadlesspressure sensor (ALPS), which generally takes the form of a stent toanchor the device within a vessel, may be employed. Such a device may beplaced in any suitable blood vessel location, such as in a femoralartery or pulmonary artery. A wearable sensor system, such as a Holtersensor system, may be used to monitor ECG activity of the patient.Regardless of whether the sensor or sensor system employed, orcomponents thereof, is implantable, wearable, part of a largerstand-alone device, or part of a blood fluid monitoring device, thesensor may monitor any suitable cardiovascular parameter of a patient.In various embodiments, the sensors or monitoring systems are configuredto monitor one or more of heart rate, heart rhythm or a variablethereof, or blood pressure. Examples of variables of heart rhythm thatmay be measured are heart rate variability (HRV), heart rate turbulence(HRT), T-wave alternans (TWA), P-wave dispersion, T-wave dispersion, Q-Tinterval, ventricular premature depolarization (VPD), or the like.

As indicated above, sensors for monitoring patient physiologicalparameters may be, or may have components that are, implantable orwearable. In embodiments, multiple sensors may be connected viatelemetry, body bus, or the like. The connected sensors may be of thesame or different type (e.g., pH or impedance). Such connected sensorsmay be placed (e.g., internal or external) for purposes of monitoring atvarious locations of the patient's body.

Monitoring may alternatively or additionally include receiving patientor physician feedback regarding the patient's state. For example, thepatient may indicate a point in time when cramping begins, which oftenhappens when too much fluid is removed. The blood fluid monitoringdevice may include an input, such as a keyboard or touch screen displayfor entering such data. Alternatively, a separate device such as apatient programmer, laptop computer, tablet computer, personal dataassistance, smart phone or the like may be used to input the data; orthe like.

Referring now to FIG. 20, a high level flow diagram of a method isdescribed. The method includes providing input 600, such as inputvariables discussed above with regard to FIG. 20, to a blood fluidremoval system. The method also includes initiating or starting 700 ablood fluid removal or dialysis session, and learning 800 from thesession. The learning 800 may be as discussed above with regard to FIG.19 with system parameters being varied and patient physiologicalparameters being monitored to determine which system parameteradjustments result in desirable patient physiologic outcomes. Thelearning may also occur over multiple sessions by monitoring patientvariables within the sessions or by monitoring patient variables betweensessions to determine how well the patient responded prior sessions topredict how well a patient will respond to future sessions (or to setinitial parameters for future sessions based on prior experiences).

For example and with reference to FIG. 21A, additional detail regardingan embodiment of a learning process that may occur during a blood fluidremoval or dialysis session is shown. The blood fluid removal ordialysis session is started 700 and the patient is monitored 810.Monitored patient parameters, such as patient physiological variables asdiscussed above, are stored 820; e.g., in memory of the blood fluidremoval system. The system parameters, such as system variablesdescribed above, which may include rate of fluid removal from the bloodor electrolyte concentration of a dialysate or replacement fluid, areadjusted 830 and the system parameters are stored 840; e.g., in memoryof the blood fluid removal, monitoring system, or dialysis system, andpatient monitoring 810 continues. The set of stored patient parameters820 are associated 850A with a set of stored system parameters 840 sothat the system may recall particular system parameters that wereemployed at the time the patient had a given set of parameters. The dataregarding the stored patient parameters 820 and the stored systemparameters 840 may be tagged with, for example, a time event toassociate the two sets of data. Of course any other suitable method ormechanism for associating the data sets may be employed. In someembodiments, the associated data, or a portion thereof, is placed in alookup table tracking the patient's history of physiological response tochanging system parameters 860A.

Referring now to FIG. 21B, an overview of a learning process that mayoccur with monitoring between blood fluid removal or dialysis sessionsis shown. Before, during or after a blood fluid removal or dialysissession is ended 899, system parameters used in the session are stored840. The system parameters, such as system variables described above,which may include rate of fluid removal from the blood or electrolyteconcentration of a dialysate or replacement fluid, as well as anyadjustments made during the session that has just ended may be stored inmemory and associated with the patient. During one or more time periodsbetween the end of the session 899) and the start of the next session700, the patient is monitored 810. Monitored patient parameters, such aspatient physiological variables as discussed above, are stored 820;e.g., in memory of the blood fluid removal system or in memory of adevice capable of communicating with, or a part of, the blood fluidremoval system. For example, if monitoring 810, or a portion thereof,occurs via an implanted device, the implantable monitoring device may beconfigured to wirelessly communicate with a blood fluid removal systemor a device capable of communicating with the blood fluid removalsystem. If monitoring includes assays or other diagnostic procedures forwhich data is presented to a user, such as a health care provider, thedata may be entered into a blood fluid removal system or device incommunication with the blood fluid removal system. The set of storedsystem parameters 840 are associated 850B with a set of patient systemparameters 820 so that the system may recall particular systemparameters that were employed in prior sessions that resulted in a givenset of patient parameters. The data regarding the stored patientparameters 820 and the stored system parameters 840 may be tagged with,for example, a time event to associate the two sets of data. Of courseany other suitable method or mechanism for associating the data sets maybe employed. In some embodiments, the associated data, or a portionthereof, is placed in a lookup table tracking the patient's history ofphysiological response to system parameters 860B. Depending on thepatient's response (patient monitoring 810) to the prior sessions, thesystem parameters may be adjusted 83) prior to beginning the nextsession 700. The patient's responses between sessions may also affectchanges made during a session.

Referring now to FIG. 21C, an overview of a learning process thataccounts for both inter-session and intra-session patient monitoring isshown. The process depicted in FIG. 21C is mainly a composite of theprocesses depicted and described above with regard to FIGS. 21A-B. Asdepicted in FIG. 21C, the process or algorithm may include associating850A system parameters 840, and adjustments thereof 830, that result ingood or bad outcomes with regard to patient parameters 820 and mayrecall those associations for later use, e.g. in the form of a lookuptable 860A for purposes of making future adjustments to systemparameters 830 based on patient response 810 within a session. Priorpatient responses occurring between prior sessions (i.e., between end ofsession 899 and beginning of session 700) may also be taken into account(e.g., associated parameters (850B) that include patient parametersobtained between sessions) by, for example, referring to lookup table860B. If, for example, changes in systems parameters (830) within asession are associated with good (effective) or bad (ineffective)patient responses (810) between sessions, similar changes may be made oravoided, as relevant, within a session. In addition, the patientresponse (810) to a prior session or the patient's condition (810)before a session may warrant adjustment of system parameters (830) priorto beginning a session (700). The patient response (810) within priorsessions may also be taken into account (e.g., by reference to historytable 860A) in making system adjustments prior to beginning a session.

A more detailed embodiment of a within-session learning algorithm, ormethod is presented in FIG. 23A. In the embodiment depicted in FIG. 22A,a patient is monitored 810 during a blood fluid removal session. It maybe desirable to determine whether data acquired from patient monitoringis out of range 813. As used herein, “out of range” means that a valueof a monitored parameter exceeds (i.e., is above or below) apredetermined range of values. The predetermined range of values may beindicative of a patient safety concern. If the data is out of range, analert may be issued 815 or the session may be stopped 817. In somecases, it may be desirable to continue with the session, even if themonitored data, or some aspect thereof is out of range. In the depictedembodiment, if the session is continued, (e.g., due to choice or to themonitored data not being out of range), data regarding the monitoredpatient parameters is stored 820 and is compared to stored patient datapreviously obtained (e.g., in a prior session or earlier in thesession). A determination may be made as to whether the present patientparameter data is less effective 823 than stored patient parameter dataresulting from system parameter adjustments 830 that occurred just priorto the current set of system parameters. If the data is determined to beless effective 823, the stored current patient parameters 820 may beassociated 851 with stored current system parameters 840; e.g., asdiscussed above. In some cases, it may be desirable to determine whetherthe current patient parameter data, or a portion or aspect thereof, isthe least effective that has been detected in the patient in a currentor previous blood fluid removal session 825; e.g. by comparing thecurrent patient data to a history of collected patient data. If thecurrent patient data is the least effective observed 825 to date, thestored current patient parameters 820 can be associated 851 with storedcurrent system parameters 840. In this way, only the “least effective”patient conditions are tracked, as opposed to all patient conditions,which can save on memory and processing power. In any case, once thepatient and system parameter data is associated 851, the systemparameters may be adjusted 830 and the process repeated.

If the present patient parameter data is determined to not be lesseffective than stored patient parameter data resulting from systemparameter adjustments that occurred just prior to the current set ofsystem parameters, a determination may be made as to whether the presentpatient parameter data is more effective 833 than stored patientparameter data resulting from system parameter adjustments 830 thatoccurred just prior to the current set of system parameters. If the datais determined to be more effective 833, the stored current patientparameters 820 may be associated 852 with stored current systemparameters 840; e.g., as discussed above. In some cases, it may bedesirable to determine whether the current patient parameter data, or aportion or aspect thereof, is the most effective that has been detectedin the patient in a current or previous blood fluid removal session 835;e.g. by comparing the current patient data to a history of collectedpatient data (e.g., “history table” in FIG. 21). If the current patientdata is the most effective observed 835 to date, the stored currentpatient parameters 820 can be associated 852 with stored current systemparameters 840. In this way, only the “most effective” patientconditions are tracked, as opposed to all patient conditions, which cansave on memory and processing power. In any case, once the patient andsystem parameter data is associated 852, the system parameters may beadjusted 830 and the process repeated.

A more detailed embodiment of a between-session learning algorithm, ormethod is presented in FIG. 22B. In the embodiment depicted in FIG. 22B,patient is monitored 810 between a blood fluid removal or dialysissessions. It may be desirable to determine whether data acquired frompatient monitoring 810 is out of range 813. If the data is out of range,an alert may be issued 815 prompting the patient to seek medicalattention or prompting a health care or an implanted system or device totake action. In some cases, a new session may be begun 700 if patientconditions warrant. If a new session is not initiated, the inter-sessionprocess may continue. In the depicted embodiment, if the process iscontinued, data regarding the monitored patient parameters is stored 820and is compared to stored patient data previously obtained (e.g.,between prior sessions). A determination may be made as to whether thepresent patient parameter data is less effective 823 than stored patientparameter data obtained between previous sessions. If the data isdetermined to be less effective 823, the stored current patientparameters 820 may be associated 851 with stored system parameters 840from the previous session that had ended 899. In some cases, it may bedesirable to determine whether the current patient parameter data, or aportion or aspect thereof, is the least effective that has been detectedin the patient between blood fluid removal sessions 825; e.g. bycomparing the current patient data to a history of collected patientdata. If the current patient data is the least effective observed 825 todate, the stored current patient parameters 820 can be associated 851with stored system parameters 840 from the previous session that hadended 899. In this way, only the “least effective” patient conditionsare tracked, as opposed to all patient conditions, which can save onmemory and processing power. In any case, once the patient and systemparameter data is associated 851, a recommendation as to systemparameters to be used in the next session may be made (e.g., the systemparameters for the future session can be set 830 based on the patientresponse or prior patient responses) can be adjusted 830 and the processrepeated until the next session begins 700.

If the present patient parameter data is determined to not be lesseffective than stored patient parameter data obtained from time periodsbetween prior sessions, a determination may be made as to whether thepresent patient parameter data is more effective 833 than stored patientparameter data obtained from between prior sessions. If the data isdetermined to be more effective 833, the stored current patientparameters 820 may be associated 852 with stored current parameters 840from the previous session that had ended 899. In some cases, it may bedesirable to determine whether the current patient parameter data, or aportion or aspect thereof, is the most effective that has been detectedin the patient in a time between sessions 835; e.g. by comparing thecurrent patient data to a history of collected patient data (e.g.,“history table” in FIG. 21). If the current patient data is the mosteffective observed 835 to date, the stored current patient parameters820 may be associated 852 with stored system parameters 840 from theprevious session that had ended 899. In this way, only the “mosteffective” patient conditions are tracked, as opposed to all patientconditions, which can save on memory and processing power. In any case,once the patient and system parameter data is associated 852,recommendation system parameters may set 830 based on the patientresponse or prior patient responses, and the process repeated until thenext session begins 700.

It will be understood that the processes or algorithms depicted in, anddiscussed above with regard to, FIGS. 22A-B may be combined (e.g., in amanner similar to the combination of FIGS. 21A and 21B into FIG. 21C).In this way, setting of system parameters for an upcoming session cantake into account how a patient responded to such parameters withinprior sessions, or altering of system parameters within a session maytake into account how a patient responded to such alterations betweenprior sessions.

Referring now to FIG. 23A, an embodiment of a method where more than onepatient parameter variable is evaluated in a manner similar to thatdescribed with regard to FIG. 22A. In the embodiment depicted in FIG.23A, two patient parameter variables are evaluated. However, it will beunderstood that any number of patient parameter variables may beevaluated by employing a method as depicted in FIG. 23A or using anyother suitable method. In the embodiment depicted in FIG. 23A, thevariables are labeled “primary” and “secondary”, as it may be desirableto prioritize patient parameter variables. For example, in some cases itmay be desirable to monitor blood pressure and attempt to achieve astable blood pressure at or near a target range throughout the sessionbecause hypotension is one of the most common side effects of bloodfluid removal sessions. That is, as long as other patient parameters arenot out of a predetermined range, the system may attempt to keep bloodpressure in check and make adjustments to that end. However, in somecases, reducing arrhythmias is the primary goal, as many patients forwhich a blood fluid removal process is indicated dire from complicationsdue to arrhythmias. If arrhythmias are determined to be the primarypatient parameter, the blood fluid removal system may attempt to keeparrhythmias in check and make adjustments to this effect without regardto other patient parameters, e.g., as long as the other patientparameters remain within acceptable limits.

The method depicted FIG. 23A includes monitoring patient parameters 810(at least a primary and secondary patient parameter), storing patientparameter data 820, and determining whether a parameter, or aspectthereof, is out of a predetermined range 813. If the parameter is out ofrange, an alert may be issued 815, the blood fluid removal session maybe stopped 817 or the session may continue. If the parameters aredetermined to not be out of range 813, the system parameters may beadjusted 843 and stored 840. A determination may then be made as towhether the primary patient parameter is less effective 843, e.g. bycomparing current patient parameter data to stored patient parameterdata resulting from system parameter adjustments that occurred justprior to the current set of system parameters. If the primary patientparameter is determined to be less effective 843, the current storedpatient parameter data may be associated 853 with the current storedsystem parameters. Alternatively or in addition, a determination may bemade as to whether the current patient parameter data regarding theprimary parameter is the least effective that has been detected in thepatient in a current or previous blood fluid removal session 845; e.g.,as discussed above with regard to FIG. 22A. If it is the leasteffective, the current stored patient parameter data may be associated853 with the current stored system parameters as described above withregard to FIG. 22A. Similarly determinations as to whether the primarypatent parameter data is more effective 853 or the most effective todate 855 can be made and stored system and patient parameters may beassociated 854. Similar determinations regarding whether the secondarypatient parameter, or a value associated therewith, is less effective863, the least effective 865, more effective 873, the most effective 875and appropriate associations 855, 856 can be made. In this manner, thesystem may identify and learn how system parameters may affectindividually monitored patient parameters, such as blood pressure, heartrate, fluid volume, and electrolyte concentration. Based on thisinformation, the system may make choices as to which system parametersmay be employed to produce results that are likely to be favorable tothe patient.

Referring now to FIG. 23B, an embodiment of a method where more than onepatient parameter variable is evaluated between blood fluid removal ordialysis sessions in a manner similar to that described with regard toFIG. 22B. In the embodiment depicted in FIG. 23B, two patient parametervariables are evaluated. However, it will be understood that any numberof patient parameter variables may be evaluated by employing a method asdepicted in FIG. 23B or using any other suitable method. In theembodiment depicted in FIG. 23B, the variables are labeled “1º” and“2º”. However, such labeling does not necessarily imply that onevariable is more important than another. While one variable may, in somecircumstances be considered more important, the labeling of “primary”and “secondary” may merely imply that the variables being monitored andtracked are different from one another.

The method depicted FIG. 23B includes ending a blood fluid removalsession 899 and storing system parameters 840 from the ended session,which may be done during the session or after the session has ended (asdepicted). The method also includes monitoring patient parameters 810(at least a primary and secondary patient parameter), storing patientparameter data 820, and determining whether a parameter, or aspectthereof, is out of a predetermined range 813. If the parameter is out ofrange, an alert may be issued 815, prompting the patient to seek medicalattention or prompting a healthcare provider or system or device to takeaction. In some cases, a blood fluid removal process can be initiated700, e.g. if warranted or desired. If the parameters are determined tonot be out of range 813 or if a blood fluid session is not initiated, adetermination may be made as to whether the primary patient parameter isless effective 843, e.g. by comparing current patient parameter data tostored patient parameter data resulting from system parameters used inthe previous session. If the primary patient parameter is determined tobe less effective 843, the current stored patient parameter data may beassociated 853 with the stored system parameters from the previoussession. Alternatively or in addition, a determination may be made as towhether the current patient parameter data regarding the primaryparameter is the least effective that has been detected in the patientbetween blood fluid removal sessions 845; e.g., as discussed above withregard to FIG. 22B. If it is the least effective, the current storedpatient parameter data can be associated 853 with the stored systemparameters as described above with regard to FIG. 22B. Similarlydeterminations as to whether the primary patent parameter data is moreeffective 853 or the most effective to date 855 can be made and storedsystem and patient parameters may be associated 854. Similardeterminations regarding whether the secondary patient parameter, or avalue associated therewith, is less effective 863, the least effective865, more effective 873, the most effective 875 and appropriateassociations 855, 856 can be made. In this manner, the system mayidentify and learn how system parameters employed in previous sessionsmay affect individually monitored patient parameters, such as bloodpressure, heart rate, fluid volume, and electrolyte concentration. Basedon this information, the system may make choices as to which systemparameters may be employed in future sessions to produce results thatare likely to be favorable to the patient.

As depicted in FIG. 23B, recommended system parameters may be set 830based on how the patient responded to the prior session or the patient'scondition prior to the upcoming session. The recommended systemparameters may be adjusted or set 830 more than once during the processof monitoring the patient between sessions or at the end of theinter-session monitoring before initiating the next blood fluid removalsession 700.

It will be understood that the processes or algorithms depicted in, anddiscussed above with regard to, FIGS. 23A-B may be combined (e.g., in amanner similar to the combination of FIGS. 21A and 21B into FIG. 21C).In this way, setting of system parameters for an upcoming session maytake into account how a patient responded to such parameters withinprior sessions, or altering of system parameters within a session maytake into account how a patient responded to such alterations betweenprior sessions.

Referring now to FIG. 24A, a flow diagram depicting a process where thecombined response of two or more patient parameters to changes in systemparameters 830 is tracked within a session. For the purposes ofconvenience some of the steps depicted and described above with regardto FIGS. 22A and 23A are omitted from FIG. 24A. However, it will beunderstood that the same or similar steps may be employed with regard tothe method depicted in FIG. 24A. In the depicted embodiment, patientparameters and system parameters are stored 857, 858 only when both theprimary and secondary patient parameters are determined to become lesseffective 843, 863 or more effective 853,873. In this manner, the systemmay identify or learn which system parameters result in desirable (orundesirable) changes in multiple patient parameters.

Referring now to FIG. 24B, a flow diagram depicting a process where thecombined response of two or more patient parameters to changes in systemparameters 830 is tracked between sessions. For the purposes ofconvenience some of the steps depicted and described above with regardto FIGS. 22B and 23B are omitted from FIG. 25B. However, it will beunderstood that the same or similar steps may be employed with regard tothe method depicted in FIG. 24B. In the depicted embodiment, patientparameters are stored 857, 858 only when both the primary and secondarypatient parameters are determined to become less effective 843, 863 ormore effective 853, 873 and can be associated with stored systemparameters 840 for the previously ended session 899. In this manner, thesystem may identify or learn which system parameters result in desirable(or undesirable) changes in multiple patient parameters.

Through the association of patient parameter data and system parameterdata as shown in FIGS. 21-24 and discussed above, a history of patientresponses, within sessions or between sessions, to changing systemparameters may be obtained. This history, which may be in the form ofone or more lookup table, may be consulted prior to or during a bloodfluid removal session to determine which system parameters, given thepatient's physiological parameters at a given point in time, are morelikely to cause the patient to respond favorably and which systemparameters are more likely to cause the patient to respond negatively.Accordingly, the system may respond by adjusting or setting parametersto those that are more likely to cause the patient to respond favorably.

For example and with reference to FIG. 25, a flow diagram is shown thatdepicts and embodiment of how stored and associated data (e.g., asdiscussed above with regard to FIGS. 21-24) can be used to determinewhich system parameters to use at a given time in or before a bloodfluid removal session. The method includes monitoring patient parameters810, within a blood fluid removal session or between sessions, andconsulting history lookup table 880, which may be generated byassociating system parameters and patient parameters as described abovewith regard to FIGS. 21-24. Monitoring the patient 810 may includemonitoring physiological variables or receiving input from the patient,a healthcare provider, or the like. A value associated with the currentpatient parameter data (obtained from monitoring 810) is compared todata regarding a corresponding value in the lookup table, and adetermination is made as to whether the current patient parameter issimilar to prior patient parameter data stored in the history table 882.By way of example, a value of a current patient parameter data set maybe determined to be similar to a corresponding value in the lookup tableif the values are within 10%. The system may consult the lookup table toidentify the closest corresponding value, if more than one correspondingvalue is within the predetermined cutoff for being considered similar(e.g., within 10%). As used herein, a “corresponding” value is a valueof the same parameter obtained at different times. The value may be amagnitude, a rate of change, an average, or the like. The parameter maybe blood pressure, heart rate, fluid volume, concentration ofelectrolyte, or the like.

If more than one parameter or value of a parameter is compared to datain the lookup table, the system may determine whether each value foreach parameter is within the predetermined cutoff for being consideredsimilar and identify a prior patient parameter data set as being mostsimilar by prioritizing or weighting parameters or by summing thepercent differences between all of the current values and thecorresponding values in the lookup table. Regardless of how the systemdetermines whether a current patient parameter data set is similar, ormost similar, to a prior patient data set stored in the history table, adetermination may be made as to whether the patient's response to thesystem parameters associated with the stored patient parameter datatable was a favorable response 884; e.g., was “more effective” or “mosteffective” as discussed above with regard to FIGS. 22-24. If the priorpatient response was determined to be a good response, the systemparameters may be set or adjusted according to the parameters stored inthe lookup table 892. If the prior patient response was considered tonot to be similar 882 or good 884, a default table may be consulted 888which contains non-patient specific system parameters that wouldgenerally be considered suitable in general circumstances or that wouldbe considered suitable for a patient presenting with the currentphysiological parameters. The system parameters may then be set oradjusted according to the parameters stored in the default table 890.

It will be understood that prior patient negative responses (e.g., “lesseffective”, “least effective to date”) may be stored in a lookup table,accessed and used in a similar manner to that described with regard tothe “good” responses in FIG. 25. In some embodiments, separate lookuptables are maintained for “more effective” responses (e.g., an“increased effectiveness” data table) and for “less effective responses”(e.g., a “decreased effectiveness” data table). In some embodiments, the“increased effectiveness” lookup table and the “decreased effectiveness”lookup table are the same data table, which stores patient parametersand associated system parameters that resulted in “more effective”,“most effective”, “less effective” or “least effective” patientparameters. As discussed above, lookup tables may include informationregarding patient data obtained within a session or between sessions.

For purposes of example and to provide some clarity with regard to howone (or a blood fluid removal or dialysis system or monitoring system)can determine whether patient parameter data is “out of range”, “moreeffective”, “less effective”, and the like (e.g., as discussed abovewith regard to FIGS. 22-24), graphical schematic data is presented inFIG. 26 showing representations of monitored data (not actual data) forblood pressure (BP), heart rate (HR), and potassium concentration in thepatient's blood ([K⁺]). In the schematic illustration, a blood fluidremoval session is initiated at T1 and is ended at T4. System parametersare changed at times T2 and T3. The patient parameters (BP, HR, [K⁺])are shown as changing in response to the changes in blood fluid removalsystem parameters and continuing to change after the session ends. Asshown, not all patient parameters will respond similarly (e.g., moreeffective or less effective) in response to a system parameter change orsession. In the depicted schematic illustrations, a desired target valueis shown for each patient parameter. If the monitored data valueachieves or approaches the target, a determination may be made that thechange in system parameter or an overall session resulted in anincreased effectiveness or “more effective” state for that parameter. Ifthe monitored data value deviates from the target, a determination maybe made that the change in system parameter or overall sessionparameters resulted in a decreased effectiveness or “less effective”state for that parameter. It will be understood that the timing of thepatient parameter response to a change in system parameters may varygreatly from patient parameter to patient parameter. In some cases,changes in a patient parameter may be observed within seconds or minutesof a change in a system parameter. In other cases, a change in a patientparameter in response to a change in a system parameter may take hoursor more to be fully appreciated or observed.

In the graphical depictions of the represented monitored data presentedin FIG. 27, a lower threshold value and an upper threshold value aredepicted by horizontal dashed lines. If the monitored data for a patientparameter exceeds the upper threshold value or crosses below the lowerthreshold value, a determination may be made that the value for thatparameter is “out of range.”

It will be understood that the condition of a patient may deterioratewith time, which is typical of patients having chronic kidney disease.Accordingly, the targets and upper and lower thresholds may vary withtime. These targets and thresholds may be modified by input from, forexample, a healthcare provider from time to time based on, e.g., thepatient's health or status of patient parameters. Alternatively, thesystem may automatically adjust target or threshold values over timebased on population data or based on data of a particular patientindicative of a generally deteriorating condition. If the target orthresholds are adjusted to or near predetermined cutoff values, an alertmay be issued to that effect.

Further, target and threshold values for one or more parameters can bemodified on a session-by-session basis. For example, if the patient isexcessively fluid overloaded prior to a given session, the target orthreshold tissue fluid levels may be adjusted upward for the next orcurrent session. The negative consequences of too much fluid removal inone session or at too fast of a rate may outweigh the negativeconsequences of higher fluid levels remaining in the patient. Additionalor more frequent fluid removal sessions may be employed to return thepatient to more desirable fluid levels.

As shown in the examples presented in FIG. 26, the patient parameterschange over time. In embodiments, values of one or more patientparameters are averaged over a period of time to account forfluctuations that may occur. The averaged value may be compared to thetarget and thresholds for determining whether a patient is improving. Byaveraging values over time, the effect of an anomalous value that maydeviate significantly from the target value or may be out of bounds maybe diminished. Of course, thresholds may be set for single occurrences,for example if the values of those occurrences may present an imminenthealth concern to the patient. In embodiments, the presence a singleoccurrence that deviates significantly from other recent occurrences mayresult in activation of a subroutine or monitoring method for detectingsimilar subsequent deviations. In embodiments, consecutive significantdeviations, a percent of significant deviations within a given number ofsamples, or the like, may result in activation or an alert or alarm.

Additional examples of systems and teachings useful in practicing theabove embodiments can be found in, for example, U.S. Provisional PatentApplication No. 61/480,532, filed on Apr. 29, 2011, and U.S. patentapplication Ser. No. 13/424,479 filed Mar. 20, 2012, both entitledELECTROLYTE AND pH MONITORING FOR FLUID REMOVAL PROCESSES, U.S. patentapplication Ser. No. 13/424,529 filed Mar. 20, 2012, entitledINTERSESSION MONITORING FOR BLOOD FLUID REMOVAL THERAPY, and U.S.Provisional Patent Application No. 61/480,544, filed on Apr. 29, 2011,and U.S. patent application Ser. No. 13/424,525 filed Mar. 20, 2012,both entitled CHRONIC pH OR ELECTROLYTE MONITORING, all whichapplications are hereby incorporated herein by reference in theirentirety to the extent that they do not conflict with the presentdisclosure.

The particular embodiments disclosed above are illustrative only, as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings provided herein. Furthermore, no limitations are intended withrespect to the details of construction or the design shown herein, otherthan as described in the claims below. It is therefore evident that theparticular embodiments disclose above may be altered or modified andthat all such variations are considered to be within the scope andspirit of the present invention.

What is claimed is:
 1. An implantable medical device, wherein theimplantable medical device is programmed to: observe cardiac cycles of asubject; calculate one or more of a first risk score and a second riskscore based upon a plurality of features of the cardiac cycle, the cycleassociated with a time index and a risk score, wherein the risk score iscalculated using a forward computational procedure; send an alert whenat least one risk score exceeds a threshold for a defined time period,for a number of time indices, or for a certain fraction or number oftime indices within a defined time period; calculate the first riskscore by comparing one or more features selected from P-IR interval, QRSwidth, Q-T interval, QT-dispersion, P-wave amplitude, P-wave peak, S-Tsegment depression, T-wave inversion, U-wave amplitude, T-wave peakamplitude, and heart rate variability to corresponding value criteria,and calculate the second risk score by comparing one or more featuresselected from QRS width, Q-T interval, P-wave amplitude, P-wave peak andT-wave amplitude to corresponding value criteria; wherein at least oneof the first risk score and the second risk score is calculated basedupon assigning values to one or more selected from feature scores P1,P2, P3, P4, P5, P6, P7, P8, P9, and P10, wherein the values are assignedas follows: P1 based upon a comparison with the feature P-R interval intime units, P2 based upon a comparison with the feature QRS width intime units, P3 based upon a comparison with the feature Q-T interval intime units, P4 based upon a comparison with the feature P-wave amplitudein potential units per time unit, P5 based upon a comparison with thefeature P-wave peak in potential units P6 based upon a comparison withthe feature of depression of the S-T segment, P7 based upon a comparisonwith the feature of inversion of the T-wave, P8 based upon a comparisonwith the feature of U-wave amplitude in potential units, P9 based upon acomparison with the feature of T-wave amplitude in potential units, andP10 based upon a comparison with the feature of heart rate variation intime units; and wherein the implantable medical device is programmed tomake a modification to a dialysis treatment received by the subjectbased upon a result of the forward computational procedure.
 2. Theimplantable medical device of claim 1, wherein the implantable medicaldevice is a pacemaker.
 3. The implantable medical device of claim 1,wherein the implantable medical device is a defibrillator.
 4. Theimplantable medical device of claim 1, wherein the implantable medicaldevice is a cardiac resynchronization device.
 5. The implantable medicaldevice of claim 1, wherein the threshold is determined based upon abaseline risk score value of the subject.
 6. The implantable medicaldevice of claim 1, wherein the values are assigned a non-zero valuebased on the following: P1 assigned a non-zero value if P-R interval isgreater than 200 msec, P2 assigned a non-zero value if QRS width isgreater than 130 msec, P3 assigned a non-zero value if Q-T interval isgreater than 200 msec, P4 assigned a non-zero value if P-wave amplitudeis less than 1 mV, P5 assigned a non-zero value if P-wave peak isgreater than 1 mV/msec, P6 assigned a non-zero value if S-T segment isdepressed, P7 assigned a non-zero value if T-wave is inverted, P8assigned a non-zero value if U-wave amplitude is greater than 2 mV, P9assigned a non-zero value if T-wave amplitude is greater than 3 mV, andP10 assigned a non-zero value if heart rate variation is less than 50msec.
 7. The implantable medical device of claim 6, wherein theimplantable medical device is programmed to determine the first riskscore by a non-weighted or weighted sum of P1, P6, P7, P8 and P10. 8.The implantable medical device of claim 6, wherein the implantablemedical device is programmed to determine the second risk score by anon-weighted or weighted sum of P2, P3, P4, P5 and P9.
 9. Theimplantable medical device of claim 1, wherein the implantable medicaldevice is programmed to determine the first risk score by a non-weightedor weighted sum of P1, P6, P7, P8 and P10.
 10. The implantable medicaldevice of claim 1, wherein the implantable medical device is programmedto determine the second risk score by a non-weighted or weighted sum ofP2, P3, P4, P5, P9 and P10.
 11. The implantable medical device of claim1, wherein the implantable medical device is further programmed toincrementally increase a risk counter for each consecutive time indexthat a risk score of the at least one risk score exceeds the thresholdvalue.
 12. The implantable medical device of claim 11, wherein thethreshold is determined based upon a baseline risk score value of thesubject.
 13. The implantable medical device of claim 1, wherein theimplantable medical device is further programmed to incrementallyincrease a risk counter for each time index within a defined time periodthat a risk score of the at least one risk score exceeds the thresholdvalue.
 14. The implantable medical device of claim 13, wherein thethreshold is determined based upon a baseline risk score value of thesubject.
 15. The implantable medical device of claim 1, wherein theimplantable medical device is programmed to modify dialysis treatment tooccur at a more frequent basis compared to a prior dialysis treatment ifblood serum potassium concentration of the patient is determined to behigh by the forward computational procedure.
 16. The implantable medicaldevice of claim 1, wherein the implantable medical device is programmedto modify dialysis treatment to use a dialysate with a non-constantconcentration of a potassium over the course of the dialysis treatmentif blood serum potassium concentration of the patient is determined tobe high by the forward computational procedure.
 17. The implantablemedical device of claim 1, wherein the implantable medical device isprogrammed to modify dialysis treatment to a longer period of timecompared to a prior dialysis treatment if blood serum potassiumconcentration of the patient is determined to be high by the forwardcomputational procedure.
 18. The implantable medical device of claim 1,wherein if blood serum potassium concentration of the patient isdetermined to be low by the forward computational procedure, theimplantable medical device is programmed to modify dialysis treatment touse a dialysate having a higher concentration of a potassium saltcompared to a prior dialysis treatment and/or the dialysis treatment ismodified to a shorter period of time compared to a prior dialysistreatment.
 19. The implantable medical device of claim 1, furthercomprising a pulse generator configured to contact a tissue of thesubject and a sensor configured to detect a response of the tissue. 20.The implantable medical device of claim 19, the implantable medicaldevice programmed to determine a concentration of potassium ions in anextracellular fluid of the subject.